Detection and ranking of entities from mobile onscreen content

ABSTRACT

Systems and methods are provided for detecting and ranking entities identified in screen content displayed on a mobile device. For example, a method includes receiving an image captured from a mobile device display for a mobile application and determining a window that includes a chronological set of images, the images each representing a respective screen captured from a display of a mobile device and having an associated timestamp. The method also includes identifying entities appearing in images in a first portion of the window using text for images in a remaining portion of the window as context to disambiguate ambiguous entity references.

RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No.14/712,679, filed on May 14, 2015, which is a non-provisional of, andclaims priority to, U.S. Provisional Application No. 62/023,736, filedon Jul. 11, 2014, entitled “User Interface Enhancements for a MobileDevice.” The subject matter of these earlier filed applications areincorporated herein by reference.

BACKGROUND

Due to the use of mobile devices, such as smartphones and tablets, userinteraction with mobile applications has been increasing. But unlikeweb-based applications, mobile applications can differ significantly inthe features they provide. For example, link structure, the userinterface, and interaction with other applications can be inconsistentfrom one app to another. Additionally, because mobile applications areconventionally closed (e.g., cannot be crawled), the actions of the usercannot be used as context to improve the user experience, such aspersonalizing search, targeting advertising, and providing personalizedsuggestions and assistance.

SUMMARY

Implementations capture an image of a screen on a mobile device atintervals and analyze the screen content via recognition tools toprovide context for improving the user experience. For example, in someimplementations, the system performs entity detection in a mobile appenvironment. To provide context for disambiguation, the system may groupsome of the captured images into a window. The window may represent afixed length of time, with some portions of the window providing contextfor entities occurring in the other portions. In some implementations,the system is adaptive so the window is larger when the user's screen isstatic (e.g. no scrolling). Entities may be disambiguated, ranked, andassociated with a user profile. In some implementations, the system maygenerate annotation data to provide personalized assistance the user.The annotation data may provide a visual cue for actionable content,entities or content relevant to the user, summary information, etc. Theannotation data may present the annotation content, and also provideadditional content, such as labels, image labels, expunge areas, etc. Insome implementations, the system may index the captured images, forexample by text and/or entities identified from an image. The system mayuse the index in various ways, such as allowing a user to search forpreviously viewed content, to provide context-based assistance, and toautomate user input. In some implementations, the system enables theuser to share a current screen or previously captured screens withanother user. In some implementations, the system may track or captureuser input actions, such as taps, swipes, text input, or any otheraction the user takes to interact with the mobile device and use thisinformation to learn and automate actions to assist the user. In someimplementations, the system may use additional data, such as thelocation of the mobile device, ambient light, device motion, etc., toenhance the analysis of screen data and generation of annotation data.

In one aspect, a mobile device includes at least one processor andmemory storing a graph-based data store of entities connected by edgesand recognized items identified by performing recognition on each of aplurality of images of screens captured on the mobile device eachrecognized item being associated with an image of the plurality ofimages. The memory may also store instructions that, when executed bythe at least one processor, cause the mobile device select a set ofimages from the plurality of images, the set representing achronological window of time, each image in the set having a timestampwithin the chronological window, and to identify entities from thegraph-based data store in the recognized items of images in a firstportion of the window using recognized items for images in a remainingportion of the window as context to disambiguate ambiguous entityreferences.

According to one aspect, a computer system includes at least oneprocessor and memory storing instructions that, when executed by the atleast one processor, cause the system to receive, from a mobile device,an image of a screen displayed on the mobile device, to identifyrecognized items for the image by performing recognition on the image,and to repeat the receiving and identifying for a plurality of images.The instructions may also include instructions that, when executed bythe at least one processor, cause the system to select a set of imagesfrom the plurality of images, the set representing a chronologicalwindow of time, each image in the set having a timestamp within thechronological window and to identify entities appearing in therecognized items for images in a first portion of the window using therecognized items for images in a remaining portion of the window ascontext to disambiguate ambiguous entity references.

According to one aspect, a method includes receiving an image capturedfrom a mobile device display for a mobile application, determining awindow that includes a chronological set of images, the images eachrepresenting a respective screen captured from a display of a mobiledevice and having an associated timestamp, and identifying entitiesappearing in images in a first portion of the window using text forimages in a remaining portion of the window as context to disambiguateambiguous entity references.

In one aspect, a method includes capturing a screen displayed on amobile device as an image, sending the image to a server, anddetermining when to repeat the capturing and sending based on userinteraction with the screen. These and other aspects can include one ormore of the following features. For example, determining when to repeatthe capturing and sending can include repeating the capturing andsending at a first interval when the screen displayed on the mobiledevice is static and repeating the capturing and sending at a secondinterval when the screen is not static. In addition or optionally, thesecond interval is no longer than one second. As another example, themethod may also include receiving data from the server generated fromthe image and using the data to personalize the screen displayed to theuser. As another example, the method may also include identifyingentities from a data graph that appear in content represented by thecaptured screen and storing, in a user profile, the entities identified.

In one general aspect, a computer program product embodied on acomputer-readable storage device includes instructions that, whenexecuted by at least one processor formed in a substrate, cause acomputing device to perform any of the disclosed methods, operations, orprocesses. Another general aspect includes a system and/or a method fordetection and ranking of entities from mobile screen content,substantially as shown in and/or described in connection with at leastone of the figures, and as set forth more completely in the claims.Another general aspect includes a system and/or a method forhighlighting important or user-relevant mobile onscreen content,substantially as shown in and/or described in connection with at leastone of the figures, and as set forth more completely in the claims.Another general aspect includes a system and/or a method for providingactions for mobile onscreen content, substantially as shown in and/ordescribed in connection with at least one of the figures, and as setforth more completely in the claims. Another general aspect includes asystem and/or a method for providing insight for mobile onscreencontent, substantially as shown in and/or described in connection withat least one of the figures, and as set forth more completely in theclaims. Another general aspect includes a system and/or a method forindexing mobile onscreen content, substantially as shown in and/ordescribed in connection with at least one of the figures, and as setforth more completely in the claims. Another general aspect includes asystem and/or a method for automating user input and/or providingassistance from interaction understanding, substantially as shown inand/or described in connection with at least one of the figures, and asset forth more completely in the claims. Another general aspect includesa system and/or a method for sharing mobile onscreen content,substantially as shown in and/or described in connection with at leastone of the figures, and as set forth more completely in the claims.

One or more of the implementations of the subject matter describedherein can be implemented so as to realize one or more of the followingadvantages. As one example, implementations may provide a consistentuser experience across mobile applications, so that similar type ofactionable content behaves the same across applications. As anotherexample, implementations provide context for personalizing certaintasks, such as ranking search results and providing assistance. Asanother example, implementations provide an interface to quicklydiscover user-relevant and content relevant content on the screen and tosurface insightful relationships between entities displayed in thecontent. As another example, implementations may allow a user of amobile device to share a screen with another user or to transfer thestate of one mobile device to another mobile device. Implementations mayalso allow a mobile device to automatically perform a task with minimalinput from the user.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features will beapparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating an example system in accordancewith the disclosed subject matter.

FIG. 2 is an example block diagram that illustrates components that canbe used in an example system, in accordance with the disclosed subjectmatter.

FIG. 3 is a block diagram illustrating another example system inaccordance with the disclosed subject matter.

FIG. 4 illustrates an example display of a mobile computing device.

FIG. 5 illustrates a flow diagram of an example process for identifyingand ranking entities displayed on a mobile computing device, inaccordance with disclosed implementations.

FIG. 6A illustrates an example display of a mobile computing device.

FIG. 6B illustrates the example display of FIG. 6A displayed withannotation data identifying actionable content, in accordance withdisclosed implementations.

FIG. 7 illustrates another example display of a mobile computing devicewith annotation data identifying actionable content, in accordance withdisclosed implementations.

FIG. 8 illustrates a flow diagram of an example process for generatingannotation data for actionable content displayed on a mobile computingdevice, in accordance with disclosed implementations.

FIG. 9 illustrates an example display of a mobile computing device withannotation data identifying user-relevant content, in accordance withdisclosed implementations.

FIG. 10 illustrates an example display of a mobile computing device withannotation data identifying content-relevant content, in accordance withdisclosed implementations.

FIG. 11 illustrates a flow diagram of an example process for generatingannotation data identifying relevant content in the display of a mobilecomputing device, in accordance with disclosed implementations.

FIG. 12A illustrates an example display of a mobile computing devicewith annotation data highlighting connections between entities found inthe content of the display, in accordance with disclosedimplementations.

FIG. 12B illustrates an example display of a mobile computing devicewith annotation data providing information about a connection betweentwo entities found in the content of the display, in accordance withdisclosed implementations.

FIGS. 13A-B illustrate a flow diagram of an example process forgenerating annotation data identifying connections between entitiesfound in the content of the display of a mobile computing device, inaccordance with disclosed implementations.

FIG. 14 illustrates a flow diagram of an example process for generatingannotation data providing information on a connection between entitiesfound in the content of the display of a mobile computing device, inaccordance with disclosed implementations.

FIG. 15 illustrates a flow diagram of an example process for generatingan index of screen capture images taken at a mobile device, inaccordance with disclosed implementations.

FIG. 16 illustrates a flow diagram of an example process for querying anindex of screen captures taken at a mobile device, in accordance withdisclosed implementations.

FIGS. 17-19 illustrate example displays for a mobile computing devicewith automated assistance from interaction understanding, in accordancewith disclosed implementations.

FIG. 20 illustrates a flow diagram of an example process for generatingannotation data with an assistance window based on interactionunderstanding, in accordance with disclosed implementations.

FIG. 21 illustrates a flow diagram of another example process forgenerating annotation data with an assistance window based on contentcaptured from a mobile device, in accordance with disclosedimplementations.

FIG. 22 illustrates a flow diagram of an example process for automatinguser input actions based on past content displayed on a mobile device,in accordance with disclosed implementations.

FIG. 23 illustrates a flow diagram of an example process for sharing animage of screen content displayed on a mobile device, in accordance withdisclosed implementations.

FIG. 24 illustrates example displays for a mobile computing device forselecting a previously captured image, in accordance with disclosedimplementations.

FIG. 25 shows an example of a computer device that can be used toimplement the described techniques.

FIG. 26 shows an example of a distributed computer device that can beused to implement the described techniques.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of a mobile content context system inaccordance with an example implementation. The system 100 may be used toprovide context for various forms of user assistance on a mobile deviceand a consistent user experience across mobile applications. Thedepiction of system 100 in FIG. 1 is a client-server system, with somedata processing occurring at a server 110. However, other configurationsand applications may be used. For example, the data processing may occurexclusively on the mobile device 170, as illustrated in FIG. 3.Furthermore, in some implementations some of the processing may be doneon the mobile device 170 and some of the processing may occur on theserver 110. In some implementations, a user of the mobile device 170 mayindicate that portions of the processing be performed at the server 110.Thus, implementations are not limited to the exact configurationsillustrated.

The mobile content context system 100 may include a data graph 190. Thedata graph 190 may be a large graph-based data store that stores dataand rules that describe knowledge about the data in a form that providesfor deductive reasoning. For example, in a data graph, information maybe stored about entities in the form of relationships to other entities.An entity may be may be a person, place, item, idea, topic, word,phrase, abstract concept, concrete element, other suitable thing, or anycombination of these. Entities may be related to each other by labelededges that represent relationships. The labeled edges may be directed orundirected. For example, the entity representing the National FootballLeague may be related to a Jaguar entity by a “has team” relationship. Adata graph with a large number of entities and even a limited number ofrelationships may have billions of connections. In some implementations,data graph 190 may be stored in an external storage device accessiblefrom server 110 and/or mobile device 170. In some implementations, thedata graph 190 may be distributed across multiple storage devices and/ormultiple computing devices, for example multiple servers. The entitiesand relationships in the data graph 190 may be searchable, e.g., via anindex. For example, the index may include text by which an entity hasbeen referred to. Thus, reference to the data graph 190 may beunderstood to include an index that facilitates finding an entity usinga text equivalent.

The mobile content context system 100 may include a server 110, whichmay be a computing device or devices that take the form of a number ofdifferent devices, for example a standard server, a group of suchservers, or a rack server system. For example, server 110 may beimplemented in a distributed manner across multiple computing devices.In addition, server 110 may be implemented in a personal computer, forexample a laptop computer. The server 110 may be an example of computerdevice 2500, as depicted in FIG. 25, or computer device 2600, asdepicted in FIG. 26. Server 110 may include one or more processorsformed in a substrate configured to execute one or more machineexecutable instructions or pieces of software, firmware, or acombination thereof. The server 110 can also include one or morecomputer memories. The memories, for example, a main memory, may beconfigured to store one or more pieces of data, either temporarily,permanently, semi-permanently, or a combination thereof. The memoriesmay include any type of storage device that stores information in aformat that can be read and/or executed by the one or more processors.The memories may include volatile memory, non-volatile memory, or acombination thereof, and store modules that, when executed by the one ormore processors, perform certain operations. In some implementations,the modules may be stored in an external storage device and loaded intothe memory of server 110.

The mobile content context system 100 may include a content engine 120and an annotation engine 130. The content engine 120 may includecomponents that analyze images of screenshots taken on a mobile deviceto determine content that can be used to provide context and assistance,as well as supporting components that index, search, and share thecontent. Annotation engine 130 may include components that use thecontent identified by the content engine and provide a user-interfacelayer that offers additional information and/or actions to the user ofthe device in a manner consistent across mobile applications. Asillustrated in FIG. 1, components of the content engine 120 and theannotation engine 130 may be executed by server 110. In someimplementations, one or more components of the content engine 120 andthe annotation engine 130 may be executed as a mobile application onmobile device 170, either as part of the operating system or a separateapplication.

FIG. 2 is a block diagram that illustrates components of the contentengine 120 and the annotation engine 130 that can be used in an examplesystem. The content engine 120 includes a recognition engine 221. Therecognition engine 221 may be configured to perform various types ofrecognition on an image, including character recognition, imagerecognition, logo recognition, etc., using conventional or laterdeveloped techniques. Thus, recognition engine 221 may be configured todetermine text, landmarks, logos, etc. from an image and the location ofthese items in the image.

The content engine 120 may also include a candidate entity selectionengine 222. The candidate entity selection engine 222 may match theitems identified by the recognition engine 221 to entities in the datagraph 190. Entity mention identification can include looking up tokensor sequences of ngrams (each an example of an item identified by therecognition engine) and matching them to entities, for example in atable that maps from the token or ngram to an entity. Entity mentionidentified can also involve several techniques, including part-of-speechtagging, dependency parsing, noun-phrase extraction, and coreferenceresolution on the identified items. Part-of-speech tagging identifiesthe part of speech that each word in the text of the document belongsto. Dependency parsing identifies the relationships between theparts-of-speech. Noun-phrase extraction identifies, or segments, nounphrases such as the phrases “Barack Obama,” “Secretary Clinton,” or“First Lady.” In other words, noun-phrase extraction aims to identifypotential mentions of entities, including the words used to describethem. Coreference resolution aims to match a pronoun or pronominal to anoun phrase. The candidate entity selection engine 222 may use anyconventional techniques for part-of-speech tagging, dependency parsing,noun-phrase extraction, and coreference resolution. “AccurateUnlexicalized Parsing” by Klein et al. in the Proceedings of the 41^(st)Annual Meeting on Association for Computational Linguistics, July 2003,and “Simple Coreference Resolution With Rich Syntactic and SemanticFeatures” by Haghighi et al. in Proceedings of the 2009 Conference onEmpirical Methods in Natural Language Processing, August 2009, which areboth incorporated herein by reference, provide examples of such methods.

Once possible entity mentions are found the candidate entity selectionengine 222 may identify each entity in the data graph 190 that may matchthe possible entity mentions in the text and/or images. For example, ifthe candidate entity selection engine 222 identifies the text “Jaguar”as a possible entity mention, the candidate entity selection engine 222may match that text, also referred to as a text mention, to threeentities: one representing an animal, one representing an NFL team, andthe third representing a car. Thus, the text mention has three candidateentities. It is understood that entities may be associated with text orwith images and logos. For example, a picture of Big Ben may beassociated with an entity representing Big Ben in the data graph.Similarly, a picture of President Obama may be associated with an entityrepresenting Barack Obama in the data graph.

The content engine 120 may also include an entity disambiguation engine223. The entity disambiguation engine 223 determines a winner from amongthe candidate entities for a text mention. The disambiguation engine 223may include a machine-learning algorithm that uses conventional entitydisambiguation signals as well as signals unique to a mobile applicationenvironment. The entity disambiguation engine 223 may also assign a rankto the disambiguated entities. Detection of entities in text as a useris surfing the Internet in a web-browser-based environment has beenused, with user consent, to provide context for improving the userexperience, for example by personalizing search, targeting advertising,and providing user assistance. But as users move away from web-basedbrowsers to using mobile devices, such context is lost because mobileapplications are closed and cannot be crawled. Thus, as a user performsmore tasks using mobile apps, user context information is lost. Thecontent engine 120 provides a method of capturing the context tomaintain in a mobile environment the personalized user experienceprovided in a web-browser based environment. In some implementations,the disambiguation engine 223 may operate over a window of screenshots,so that screen capture images that come before and after a particulartime period can be used as context for disambiguating entities found inthe center of the window. The entities detected in the screen captureimages may be stored, for example in screen capture index 118, where thedetected entity is a key value. After disambiguating the entities, thedisambiguation engine 223 may rank the entities and store the rankings,for example as ranked entities and collections 117. In someimplementations, the ranking and entity information may be stored aspart of screen capture index 118. In some implementations, ranksdetermined over a short period of time may be stored in the screencapture index 118 and ranks for entities over a longer period of timemay be stored in ranked entities and collections 117. Collections ofentities may represent entities with a common type or some other commoncharacteristic. Thus, the system may cluster entities into one or morecollections based on the characteristics. For example, a collection maybe Italian restaurants, horror movies, luxury cars, etc.

The content engine 120 may also include an indexing engine 224. Theindexing engine 224 may index a screen capture image according to thetext, entities, images, logos, etc. identified in the image. Thus, forexample, the indexing engine 224 may generate index entries for animage. The index may be an inverted index, where a key value (e.g.,word, phrase, entity, image, logo, etc.) is associated with a list ofimages that have the key value. The index may include metadata (e.g.,where on the image the key value occurs, a rank for the key value forthe image, etc.) associated with each image in the list. In someimplementations, the index may also include a list of images indexed bya timestamp. Because the indexing engine 224 may use disambiguatedentities, in some implementations, the indexing engine 224 may update anindex with non-entity key items at a first time and update the indexwith entity key items at a second later time. The first time may beafter the recognition engine 221 is finished and the second time may beafter the disambiguation engine 223 has analyzed a window of images. Theindexing engine 224 may store the index in memory, for example screencapture index 118 of FIG. 1.

The content engine 120 may also include a query engine 225. The queryengine 225 may use the screen capture index 118 generated and maintainedby the indexing engine 224 to respond to queries. The query engine 225may return a list of screen capture images as a search result. In someimplementations, the query engine 225 may generate a user display of theresponsive screen capture images, for example in a carousel or otherscrollable list. In some implementations, the content engine 120 mayalso include a screen sharing engine 226. The screen sharing engine 226may enable a user of the mobile device to share a captured screen with adesignated recipient. The captured screen may be a current image, or anindexed image. If the user chooses to share a series of screens, thescreen sharing engine 226 may also stitch the images into a larger imagethat is navigable, making the resulting image easier to view for therecipient. The screen sharing engine 226 may also provide user inputdata that corresponds with a shared screen, when requested by the user,to the recipient device.

The annotation engine 130 may include components that build annotationinformation designed to be integrated with the screen of the mobiledevice. The annotation information may be an overlay displayed on top ofthe screen being displayed, an underlay displayed behind the screenbeing displayed, or information configured to be added to the currentscreen in the display buffer of the mobile device. In other words, theannotation information represents information added to a screengenerated at the mobile device, whether displayed over, under, orintegrated into the screen when it is displayed. The various componentsof the annotation engine 130 may generate various types of annotationdata. The annotation data may be configured to be displayed with ascreen on the mobile device so that only the visual cues, labels,images, etc., included in the annotation data are visible. In additionor alternatively, the annotation data may include expunge areas that arevisible over the screen and hide or mask corresponding areas of thescreen on the mobile device. For example, an expunge area may hidepasswords, offensive language, pornographic images, etc. displayed onthe screen.

For example, the annotation engine 130 may include an actionable contentengine 232. Actionable content includes any content in the screencapture image that can be associated with a type of action. For example,the actionable content engine 232 may use templates to identify textthat represents phone numbers, email addresses, physical addresses,etc., with each template having an associated action. For example, phonenumbers may be associated with a “dial now” action, email addresses maybe associated with a “compose a new message” action, street addressesmay be associated with a “view on map” action, etc. In someimplementations, the user of the mobile device may select a defaultaction for each template (e.g., each type of actionable content). Forexample, the user may choose to associate email addresses with an “addto contacts” action instead of a “compose message” action. In someimplementations, the system may determine the action dynamically. Forexample, the system may look for an email address or phone number in acontacts data store, either on the mobile device or associated with anaccount for the user. If the phone number is found, the system may usethe “dial now” action and if the phone number is not found the systemmay provide the user with the opportunity to choose a “dial now” actionand an “add to contacts” action. In addition to template-based textitems, actionable content may include entities identified in the text,for example by the candidate entity selection engine 222 and thedisambiguation engine 223. The action associated with an entity may beto bring up a short description or explanation of the entity. Forexample, the system may generate the description from properties and/orrelationships of the entity in the data graph 190 or may open a wikipage or a knowledge panel describing the entity. A knowledge panel is acollection of information that describes an entity and may be derivedfrom relationships between the entity and other entities or entityproperties/attributes in a data graph.

When the actionable content engine 232 finds actionable content, it maygenerate annotation data that includes a visual cue for each item ofactionable content. The visual cue may be any cue that sets theactionable content apart from non-actionable content. For example,visual cues may include, but are not limited to, highlighting,underlining, circling, outlining, and even darkening out or obscuringnon-actionable content. Each visual cue in the annotation data may beassociated with an action and configured to detect a selection thatinitiates the action. The visual cue thus, acts like a hyperlink in anHTML-based document. Because the mobile content context system 100 canprovide the annotation data for any mobile application running on themobile device, actions are consistent across mobile applications. Insome implementations, the actionable content engine 232 may identify toomany actionable content items in one screen capture image. In such asituation, the actionable content engine 232 may generate a visual cuefor the more relevant entities, for example those more highly ranked inthe search index 118 or the ranked entities and collections 117.

The annotation engine 130 may also include a relevant content engine233. The relevant content engine 233 may annotate content that isimportant or relevant to the user of the mobile device. Content may beimportant or relevant because it summarizes a body of text or because itranks highly with regard to user preferences. For example, the relevantcontent engine 233 may identify entities in the content of a screencapture image as of particular interest based on the rank of the entity,for example in the ranked entities and collections 117 data store. Insome implementations, the relevant content engine 233 may determinewhether the entity is part of a structure element, such as one of a listof items. If so, the relevant content engine 233 may generate annotationdata that includes a visual cue for the entire structure element, forexample highlighting the entire list entry and not just the text orimage representing the entity. This may enable a user to more quicklynotice a relevant item in a list of items displayed on the screen of themobile device. As another example, the relevant content engine 233 mayidentify a body of text, e.g., an article or a paragraph, and useconventional summarization techniques to identify elements of the bodyof text that effectively summarize the body. The elements that summarizethe body of text are considered content-relevant and the relevantcontent engine 233 may generate annotation data that highlights theseelements. Such highlighting may draw the user's attention to thesummary, allowing the user to more quickly identify the main point ofthe body of text. The relevant content engine 233 may work inconjunction with the actionable content engine 232. For example, in someimplementations, the visual cue for relevant content may be highlightingwhile the actionable content may be identified by underlining orcircling.

The annotation engine 130 may also include an entity insight engine 234.The entity insight engine 234 may provide an interface for surfacinginformation about the entities found in a screen captured from themobile device. In some implementations, the entity insight engine 234may generate annotation data for entities found in the image of themobile screen. The annotation data may include a visual cue for eachentity, similar to the actionable content engine 232. The visual cue maybe configured to respond to an insight selection action. The insightselection action may be a long press, for example. The entity insightengine 234 and the actionable content engine 232 may work together togenerate one set of annotation data. A short press may initiate theaction associated with the entity and a long press may initiate theinsight interface, which provides the user with additional informationabout how the entities displayed in the screen are related. For example,if the user performs a long press on the visual cue for the entity, thesystem may respond by generating annotation data that shows whichentities on the screen are related to the selected entity in the datagraph 190. In some implementations, the annotation data may include aline drawn between the entity and its related entities. In someimplementations the line may be labeled with a description of therelationship. This may work best when there are few related entitiesdisplayed on the screen. If the annotation data does not include alabeled line, selection of the line (or other indication that theentities are related) may provide a text description of how the entitiesare related. The text description may be based on information stored inthe data graph 190. The text description may also be based on previousco-occurrences of the two entities in a document. For example, if thetwo entities co-occur in a recent news article, the system may use thetitle of the news article as the text description.

In some implementations, if the user performs an insight selectionaction on two entities (e.g., does a long press on the visual cues fortwo entities at the same time), the entity insight engine 234 mayprovide the text description of how the entities are related inannotation data. In some implementations, the text description andentity relations may be included in the annotation data but may beinvisible until the user performs the insight selection of an entity (orof two entities at the same time). In some implementations, when theentity insight engine 234 receives a second insight selection of thesame entity the entity insight engine 234 may search previously capturedscreens for entities related in the data graph 190 to the selectedentity. For example, the entity insight engine 234 may determineentities related to the selected entity in the data graph 190 andprovide these entities to the query engine 225. The query engine 225 mayprovide the results (e.g., matching previously captured screen images)to the mobile device.

The annotation engine 130 may also include automated assistance engine231. The automated assistance engine 231 may use the information foundon the current screen (e.g., the most recently received image of thescreen) and information from previously captured screens to determinewhen a user may find additional information helpful and provide theadditional information in annotation data. For example, the automatedassistance engine 231 may determine when past content may be helpful tothe user and provide that content in the annotation data. For example,the automated assistance engine 231 may use the most relevant orimportant key values from the image as a query issued to the queryengine 225. The query engine 225 may provide a search result thatidentifies previously captured screens and their rank with regard to thequery. If any of the returned screens have a very high rank with regardto the query the automated assistance engine may select a portion of theimage that corresponds to the key item(s) and use that portion inannotation data. As another example, the automated assistance engine 231may determine that the current screen includes content suggesting anaction and provide a widget in the annotation data to initiate theaction or to perform the action. For example, if the content suggeststhe user will look up a phone number, the widget may be configured tolook up the phone number and provide it as part of the annotation data.As another example, the widget may be configured to use recognized itemsto suggest a further action, e.g., adding a new contact. A widget is asmall application with limited functionality that can be run to performa specific, generally simple, task.

The annotation engine 130 may also include expunge engine 235. Theexpunge engine 235 may be used to identify private, objectionable, oradult-oriented content in the screen capture image and generateannotation data, e.g. expunge area, configured to block or cover up theobjectionable content or private content. For example, the expungeengine may identify curse words, nudity, etc. in the screen captureimage as part of a parental control setting on the mobile device, andgenerate expunge areas in annotation data to hide or obscure suchcontent. The expunge engine 235 may also identify sensitive personalinformation, such as a password, home addresses, etc. that the user maywant obscured and generate annotation data that is configured to obscuresuch personal information from the screen of the mobile device.

Returning to FIG. 1, the mobile content context system 100 may includedata stores associated with a user account or profile. The data storesare illustrated in FIG. 1 as residing on server 110, but one or more ofthe data stores may reside on the mobile device 170 or in anotherlocation specified by the user. The data stores may include the screencapture events 113, ranked entities and collections 117, screen captureindex 118, event actions 114, and default actions 115. The data storesmay be stored on any non-transitory memory. The screen capture events113 may include the images of screens captured from the mobile device170. The screen capture events 113 may also include candidate entitiesidentified by the content engine 120. The screen capture events 113 maybe used by the content engine 120 to provide a window in which todisambiguate the candidate entities. The ranked entities and collections117 may represent rankings for the various entities identified in thescreen capture images. The rank of an entity with respect to aparticular screen capture image may be stored, for example, as metadatain the screen capture index 118. In addition or alternatively, the rankof an entity may also represent the rank of an entity over a period oftime e.g., how long an entity has been on the screen and whether theentity appeared in different contexts (e.g., different mobileapplications). Thus, the ranked entities and collections 117 may includean indication of how relevant an entity is to the user. The collectionsin the ranked entities and collections 117 may represent a higher-levelconcepts that an entity may belong to, such as “horror movies”. Theentities may be grouped into collections and ranked based on thecollection.

The screen capture index 118 may be an inverted index that stores keyvalues and lists of images (e.g., images stored in screen capture events113), that include the key values. The key values may be text, entities,logos, locations, etc. discovered during recognition by the contentengine 120. Thus, when candidate entities are selected by disambiguationfor a particular screen capture image the indexing engine may add theparticular screen capture image to the list associated with eachdisambiguated entity. The image may be associated with a timestamp, forexample in screen capture events 113. In some implementations, thescreen capture index 118 may include an index that orders the images bytimestamp. The screen capture index 118 may also include metadata abouteach image, such as a rank for the key value for the image, coordinatesin the image where the key value can be found, etc. In someimplementations, the user may specify how long screen capture images arekept in the screen capture events 113 and the screen capture index 118.

The default actions 115 may include the default actions for one or moretypes of actionable content. For example, a phone number type may havean “initiate call” action or an “add new contact” action. The user mayspecify and modify the default action. The default actions 115 may beused by the actionable content engine when generating the annotationdata.

The event actions 114 represent default event actions or widgets toprovide assistance for actions suggested in a screen capture image. Eachsuggested action may be associated with a default event action, forexample in a model that predicts actions based in interactionunderstanding.

The mobile content context system 100 may also include mobile device170. Mobile device 170 may be any mobile personal computing device, suchas a smartphone or other handheld computing device, a tablet, a wearablecomputing device, etc., that operates in a closed mobile environmentrather than a conventional open web-based environment. Mobile device 170may be an example of computer device 2500, as depicted in FIG. 25.Mobile device 170 may be one mobile device used by user 180. User 180may also have other mobile devices, such as mobile device 190. Mobiledevice 170 may include one or more processors formed in a substrateconfigured to execute one or more machine executable instructions orpieces of software, firmware, or a combination thereof. The mobiledevice 170 may thus include one or more computer memories configured tostore one or more pieces of data, either temporarily, permanently,semi-permanently, or a combination thereof. The mobile device 170 maythus include mobile applications 175, which represent machine executableinstructions in the form of software, firmware, or a combinationthereof. The components identified in the mobile applications 175 may bepart of the operating system or may be applications developed for amobile processing environment. Conventionally, mobile applicationsoperate in a closed environment, meaning that the user employs separateapplications to do activities conventionally performed in a web-basedbrowser environment. For example, rather than going to hotels.com tobook a hotel, a user of the mobile device 170 can use a mobileapplication in mobile applications 175 provided by hotels.com. Themobile device 170 may also include data 177, which is stored in thememory of the mobile device 170 and used by the mobile applications 175.FIG. 3 includes more detail on the components of the mobile applications175 and data 177.

The mobile device 170 may be in communication with the server 110 andwith other mobile devices 190 over network 160. Network 160 may be forexample, the Internet, or the network 160 can be a wired or wirelesslocal area network (LAN), wide area network (WAN), etc., implementedusing, for example, gateway devices, bridges, switches, and/or so forth.Network 160 may also represent a cellular communications network. Viathe network 160, the server 110 may communicate with and transmit datato/from mobile devices 170 and 190, and mobile device 170 maycommunicate with mobile device 190.

The mobile content context system 100 represents one exampleconfiguration and implementations may incorporate other configurations.For example, some implementations may combine one or more of thecomponents of the content engine 120 and annotation engine 130 into asingle module or engine, one or more of the components of the contentengine 120 and annotation engine 130 may be performed by the mobiledevice 170. As another example one or more of the data stores, such asscreen capture events 113, screen capture index 118, ranked entities andcollections 117, event actions 114, and default actions 115 may becombined into a single data store or may distributed across multiplecomputing devices, or may be stored at the mobile device 170.

FIG. 3 illustrates a block diagram illustrating another example systemin accordance with the disclosed subject matter. The example system ofFIG. 3 illustrates an example of the mobile content context system 300operating using just the mobile device 170 without server 110. Of courseit is understood that implementations include the mobile device 170 anda server where one or more of the components illustrated with dashedlines may be stored or provided by the server. Thus, the mobile device170 of FIG. 3 may be an example of the mobile device 170 of FIG. 1.

The mobile applications 175 may include one or more components of thecontent engine 120 and the annotation engine 130, as discussed abovewith regard to FIG. 2. The mobile applications 175 may also includescreen capture application 301. The screen capture application 301 maybe configured to capture the current screen, e.g. by copying or readingthe contents of the device's frame buffer at intervals. The interval canbe small, for example every half second or every second. In someimplementations, the screen capture application 301 may be configured tocapture the screen every time a touch event occurs (e.g., every time theuser touches the screen to scroll, zoom, click a link etc.) or when thedevice transitions from one mobile application to another mobileapplication. In some implementations, the screen capture application 301may increase the interval at which a screen capture occurs when thescreen does not change. In other words, when the screen is static, thescreen capture application 301 may capture images less often. The screencapture application 301 may provide the captured screen images andmetadata to the recognition engine 221, which may be on the mobiledevice 170 or a server, such as server 110. The metadata may include thetimestamp, the mobile device type, a mobile device identifier, themobile application running when the screen was captured, e.g., theapplication that generated the screen, etc. In some implementations, themobile applications 175 may include the recognition engine 221, whichstores the captured image and metadata and any key values identified inthe image. For example, the stored image may be stored in screen captureevents 360 on the mobile device 170 or may be sent to the server 110 andstored in screen capture events 113.

In addition to capturing images of the screen of the mobile device 170,the screen capture application 301 may also capture user input actiondata 351. User input action data 351 represents user input actions suchas taps, swipes, text input, or any other action the user takes tointeract with the mobile device 170. The user input action data 351 mayrecord a timestamp for each action that indicates when the actionoccurred. The user input action data 351 may also record the screencoordinates for a touch action, beginning and ending coordinates for aswipe action, and the text entered for keyboard actions. If the userperforms a multiple finger action, the input action data 351 may includemultiple entries with the same timestamp. For example if the user“pinches” with two fingers to zoom out, the screen capture application301 may record one entry in the user input action data 351 for the first(e.g., index finger) digit and a second entry in the user input actiondata 351 for the second (e.g., thumb) digit, each having the sametimestamp. The input action data 351 may be used to automate some tasks,as explained herein in more detail. The user input action data 351 maybe indexed by timestamp or stored in timestamp order. The user of themobile device 170 may control when the screen capture application 301 isactive. For example, the user may specify that the screen captureapplication 301 is active only when other specified mobile applications175 are running (e.g., only when in a social media mobile application).The user may also manually turn the screen capture application on andoff, for example via a settings application. In some implementations,the user may turn the capture of user input data on and offindependently of turning the screen capture functionality off.

In some implementations, the screen capture application 301 may alsocapture additional device information, such as which applications areactive, the location of the device, the time of day, ambient light,motion of the device, etc. The system may use this additional deviceinformation to assist in content analysis (e.g., entity disambiguation),annotation data generation (e.g., reducing the quantity of annotationswhen the device is moving, deciding what content is most relevant), etc.In some implementations, the screen capture application 301 may providethis additional information to the content engine and/or annotationengine.

The screen capture application 301 may use annotation data 352 tointegrate the additional information provided in annotation data 352with a current screen. For example, when the screen capture application301 receives annotation data 352, the screen capture application 301 maycombine the annotation data with the current display. In someimplementations, the annotation data may be generated as an overlay, asan underlay, or interleaved with the current screen in the displaybuffer. The annotation data may be stored in annotation data 352, forexample. Each annotation data entry may be associated with a timestamp.In some implementations, the screen capture application 301 may beconfigured to verify that the currently displayed screen is similarenough to the captured screen image before displaying the annotationdata. For example, the annotation data may include coordinates for theportion of the image that corresponds with one or more visual cues inthe annotation engine, and the screen capture application 301 maycompare the image portion represented by the coordinates with the samecoordinates for the currently displayed image. In some implementations,the screen capture application 301 may be configured to look a shortdistance for visual elements similar to those for a visual cue. Iffound, the screen capture application 301 may adjust the position of thevisual cues in the annotation data to match the movement of theunderlying screen. In some implementations, the system may display theannotation data until the user scrolls or switches mobile applications.In some implementations the annotation data 352 may include the imagedata for the coordinates of each visual cue. In some implementations,the mobile device 170 may store previously captured screen images for afew seconds, for example in screen capture events 360, and these storedimages may be used for comparison with the current screen. In suchimplementations, the annotation data 352 may have the same timestamp asthe image it was generated for so that the system can easily identifythe screen capture image corresponding to the annotation data.

The mobile applications 175 may also include application automationengine 302. The application automation engine 302 may be configured touse previously captured screen images and user input action data toautomatically perform tasks or automatically change the state of themobile device. For example, after selecting a previously captured imagefrom a search result, the application automation engine 302 may try totake the user back to the mobile application that generated the screenand use the user input actions to re-create the series of interactionsthat resulted in the captured image. Thus, the application automationengine 302 may allow the user to jump back to the place in theapplication that they had previously been. Jumping to a specific placewithin a mobile application is changing the state of the mobile device.In some implementations, the application automation engine 302 mayenable the user to switch mobile devices while maintaining context. Inother words, the user of mobile device may share a screen and user inputactions with a second mobile device, such as mobile device 190, and theapplication automation engine 302 running on mobile device 190 may usethe sequence of user input actions and the shared screen to achieve thestate represented by the shared screen. In some implementations, theapplication automation engine 302 may be configured to repeat somepreviously performed action using minimal additional data. For example,the application automation engine 302 may enable the user to repeat thereservation of a restaurant using a new date and time. Thus, theapplication automation engine 302 may reduce the input provided by auser to repeat some tasks.

The mobile applications 175 may also include screen sharing application303. The screen sharing application 303 may enable the user of themobile device to share a current screen, regardless of the mobileapplication running. The screen sharing application 303 may also enablea user to share a previously captured screen with another mobile device,such as mobile device 190. Before providing the image of the screen tobe shared, the screen sharing application 303 may provide the user ofthe mobile device 170 an opportunity to select a portion of the screento share. For example, the user may select a portion to explicitly shareor may select a portion to redact (e.g., not share). Thus, the usercontrols what content from the image is shared. Screen sharingapplication 303 may enable the user to switch mobile devices whilekeeping context, or may allow the user to share what they are currentlyviewing with another user. The mobile device 170 may be communicativelyconnected with mobile device 190 via the network 160, as discussed abovewith regard to FIG. 1. In some implementations, the screen sharingapplication 303 may share captured screens and input action sequencesvia a server that the mobile device 170 and the mobile device 190 areeach communicatively connected to.

The mobile applications 175 may also include event help applications304. Event help applications 304 may be widgets that surface informationfor an action. For example, annotation data 352 may include annotationdata that launches a widget to complete the annotation data 352 beforeit is displayed. The widget may, for example, query the calendar data onthe mobile device 170 to show availability for a time frame specified inthe annotation data. The result of the query may be displayed with thecurrent screen (e.g., overlay, underlay, interlaced, etc.). As anotherexample, the widget may obtain contact information, such as a phonenumber or an email address from the contacts data stored on the mobiledevice 170. Thus, event help applications 304 may include variouswidgets configured to surface information on the mobile device 170 thatcan be provided as data in an assistance window for the user.

When stored in data 177 on the mobile device 170, the data graph 356 maybe a subset of entities and relationships in data graph 190 of FIG. 1,especially if data graph 190 includes millions of entities and billionsof relationships. For example, the entities and relationships in datagraph 356 may represent the most popular entities and relationships fromdata graph 190, or may be selected based on user preferences. Forexample, if the user has a profile, entities and relationships may beselected for inclusion in data graph 356 based on the profile. The otherdata stores in data 177 may be similar to those discussed above withregard to FIG. 1. Specifically the screen capture index 355 may besimilar to screen capture index 118, the ranked entities 357 may besimilar to ranked entities and collections 117, the screen captureevents may be similar to screen capture events 113, the event actions359 may be similar to event actions 114, and the default actions 358 maybe similar to the default actions 115.

The mobile content context system 300 represents one exampleconfiguration and implementations may incorporate other configurations.For example, some implementations may combine one or more of thecomponents of the screen capture application 301, the applicationautomation engine 302, the screen sharing application 303, the eventhelp applications 304, the content engine 120, and the annotation engine130 into a single module or engine, and one or more of the components ofthe content engine 120 and annotation engine 130 may be performed by aserver. As another example one or more of the data stores, such asscreen capture events 360, screen capture index 355, ranked entities357, event actions 359, and default actions 358 may be combined into asingle data store or may distributed across multiple computing devices,or may be stored at the server.

To the extent that the mobile content context system 100 collects andstores user-specific data or may make use of personal information, theusers may be provided with an opportunity to control whether programs orfeatures collect the user information (e.g., information about a user'ssocial network, social actions or activities, user input actions,profession, a user's preferences, or a user's current location), or tocontrol whether and/or how to receive content that may be more relevantto the user. In addition, certain data may be treated in one or moreways before it is stored or used, so that personally identifiableinformation is removed. For example, a user's identity may be treated sothat no personally identifiable information can be determined for theuser, or a user's geographic location may be generalized where locationinformation is obtained (such as to a city, ZIP code, or state level),so that a particular location of a user cannot be determined. Thus, theuser may have control over how information is collected about the userand used by a mobile content context system.

Identifying Entities Mentioned in Mobile On-Screen Content

In order to provide context and personalized assistance in a mobileapplication environment, disclosed implementations may identify, withuser consent, entities displayed on the screen of a mobile device.Implementations may use a window of screen capture images to improveentity disambiguation and may use signals unique to the mobileenvironment in disambiguating and ranking entities. For example, thesystem may use mobile application metadata to adjust probability priorsof candidate entities. Probability priors are probabilities learned bythe entity detection engine (e.g., a mention of jaguar has an 70% chanceof referring to the animal, a 15% chance of referring to the car and a5% chance of referring to the football team). The system may adjustthese learned priors based on a category for the mobile application thatgenerated the captured image. For example, if the window is made up ofscreens from an auto-trader or other car-related mobile application, thesystem may increase the probability prior of candidate entities relatedto a car. Implementations may also use signals unique to the mobileenvironment to set the window boundaries. Because the amount of onscreencontent is limited, performing entity detection and disambiguation usinga window of images provides a much larger context and more accurateentity disambiguation.

FIG. 4 illustrates an example display 400 of a mobile computing device.In the example of FIG. 4, the display is from a mobile application thatsearches for new and used cars for sale. The display may be a display ofa mobile device, such as mobile device 170 of FIG. 1 or 3. The display400 includes some static items 410 that are always on the screen whilethe mobile application is open. The display also includes a list of carsthat are displayed to the user. The display 400 may be captured at amobile device and provided to a content engine that performs recognitionon the image, identifies possible entity mentions, and disambiguates andranks the entities found. For example, the term Jaguar 405 in the imageof the display may be a possible entity mention, as are Acura, Sedan,Luxury Wagon, BMW, Cloud, Silver, etc.

FIG. 5 illustrates a flow diagram of an example process 500 foridentifying and ranking entities displayed on a mobile computing device,in accordance with disclosed implementations. Process 500 may beperformed by a mobile content context system, such as system 100 of FIG.1 or system 300 of FIG. 3. Process 500 may be used to identify entitiesin the content of a display of a mobile device to provide context andpersonalized assistance in a mobile environment. Process 500 may beginby receiving an image of a screen captured on the mobile device (505).The captured image may be obtained using conventional techniques. Thesystem may identify recognized items by performing recognition on theimage of the captured screen (510). Recognized items may be textcharacters or numbers, landmarks, logos, etc. located using variousrecognition techniques, including character recognition, imagerecognition, logo recognition, etc. Thus, recognized items may includewords as well as locations, landmarks, logos, etc.

The system may find candidate entities based on the recognized items(515). For example, the system may perform part-of-speech tagging,dependency parsing, noun-phrase extraction, and coreference resolutionusing any conventional techniques for finding possible entity mentionsand determining what entities may correspond to each entity mention. Thesystem may also look for a fixed set of names, images, or ngrams in therecognized items. For example, the system may look for the textequivalent or image of a particular set of entities in the recognizeditems. The system may store the candidate entities, the recognizeditems, and the image as a screen capture event (520). This enablessubsequent use of the recognized items and candidate entities indisambiguation and ranking of the entities. In some implementations thecandidate entities and recognized items may be temporarily stored untilentity disambiguation is complete and the image indexed.

The system may then determine whether a window is closed (525). A windowrepresents a sequence of captured screen images for the same mobileapplication, or for two mobile applications when the second mobileapplication was launched from the first mobile application. A userswitching from one mobile application to a second mobile application maybe considered a context switch (e.g., the user is starting a new task),and including screen capture images from both applications may providefalse context signals when disambiguating entities found on images fromthe second mobile application. Thus, switching applications may breakthe window boundary and force the window to close. However, if the userswitches to the second application from within the first mobileapplication, the context may be helpful. Therefore, the system may notbreak a window boundary and forcibly close the window due to this typeof user action. Thus, when a user switches from a first mobileapplication to a second mobile application, for example by returning toa home screen, the system may consider the window closed for the firstmobile application and begin another window for the second mobileapplication. But when the user selects a link that opens the secondapplication, the system may not forcibly close the window and maycontinue to use screen capture images for the first application ascontext.

In addition to forcibly closing a window, the system may consider awindow closed when the window reaches a pre-specified size, for examplecovering a pre-specified length of time, including a pre-specifiednumber of images, or including a pre-specified quantity of unique entitymentions (e.g., tokens). The latter two options have the advantage ofbeing adaptive so that detection is performed over a longer window whenthe screen is static. When the window size is met, for example when thesystem has screen capture images that span the length of time or has thepre-specified number of images or unique tokens, the window may beconsidered closed (525, Yes). If there are not enough screen capturesfor a window (525, No), the system may continue receiving (505) andanalyzing screen captured images (510, 515) and storing them as screencapture events (520) until the window size is reached or the window isforcibly closed, for example from a change from one mobile applicationto another mobile application.

When a window is closed (525, Yes), the system may form a chronologicalwindow from a plurality of screen capture events (530). As indicatedabove, the window may include sequential screen capture events or, inother words, images that represent a chronological time period. Becausescreen capture images may be received on a regular basis, for exampleone to two images every second, the window may be a rolling window. Forexample, a first window may include screen captures during time t1-t10,a second may include screen captures during time t5-t15, a third mayinclude screen captures during time t10-t20, etc. As another example, afirst window may include the first 5 screen captures for an application,a second window may include the first 10 screen captures, the thirdwindow may include screen captures 5-15, etc. Thus, process 500 may beongoing as long as screen capture images keep arriving, and some of theimages in the window may have already had entity disambiguationperformed. Furthermore, when the window size is based on a quantity ofscreen capture images, the window may represent a variable length oftime because, for example, the system may send fewer screen captureimages when the screen on the mobile device is static. The recognizedcontent for the images included in the window may be stitched togetherto form a document, which provides the context for disambiguatingentities in the document.

A partial window may occur when the window does not cover the entirepre-specified size. For example, when a window is forcibly closed orwhen a window represents the first few seconds or images of a new window(e.g., the images captured when a new mobile application starts).Accordingly, the system may determine whether the window is a full or apartial window (535). When the window is full (535, No), the system mayperform entity disambiguation on the candidate entities associated withthe screen capture images in a center portion of the window (540). Whenentity disambiguation is performed on a center portion of the window,the recognized items and disambiguated entities associated with imagesin the first portion of the window, and the recognized items andcandidate entities in a last portion of the window may be used toprovide context for entity disambiguation in the center portion. Forexample, entity disambiguation systems often use a machine learningalgorithm and trained model to provide educated predictions of whatentity an ambiguous entity mention refers to. The models often includeprobability priors, which represent the probability of the ambiguousentity mention being the particular entity. For example, the trainedmodel may indicate that the term Jaguar refers to the animal 70% of thetime, a car 15% of the time and the football team 5% of the time. Theseprobability priors may be dependent on context, and the model may havedifferent probability priors depending on what other words or entitiesare found close to the entity mention. This is often referred to acoreference.

In addition to using these traditional signals, the system may also takeinto account signals unique to the mobile environment. For example, thesystem may adjust the probability priors based on a category for themobile application that generated the screen captured by the image. Forexample, knowing that a car search application or some other car-relatedapplication generated the display 400, the system may use this as asignal to increase the probability prior for the car-related entityand/or decrease the probability prior for any entities not car related.In the example of FIG. 4, the system may boost the probability prior ofJaguar the car over Jaguar the animal for mention 405 based on the typeor category for the mobile application. Once probabilities have beencalculated for each candidate entity for a particular entity mention,the system selects the candidate entity with the highest probability asthe discovered entity for the mention. If the probabilities are tooclose, the system may not select an entity and the mention does notcorrespond to a discovered entity. Such mentions may be consideredambiguous mentions. In some implementations, entity disambiguation maybe accomplished via a classifier (e.g., neural network) that takes asinput a candidate and context features. In some implementations, entitydisambiguation can be a weighted combination of the entity name prioralong with scores for supporting entities.

If the window is a partial window (535, Yes), the system may performentity disambiguation on the candidate entities associated with theimages in the partial window (545), using techniques similar to thosediscussed above with regard to step 540.

Once entities have been disambiguated, resulting in discovered entities,the system may drop outlier entities (550). Outlier entities arediscovered entities that are not particularly relevant to the documentformed by the window. For example, the discovered entities may begrouped by category and categories that have a small quantity ofdiscovered entities may be dropped. In the example of FIG. 4, there aremany car-related entities but the entity Red Wine 415 is not car relatedand may be dropped from the discovered entities. In someimplementations, the application type may be used to determine outliers.For example, in an automobile related application the entity Red Wine ora particular category of entities unrelated to automobiles may always beconsidered an outlier regardless of the number of entities in thecategory. In some implementations, step 550 is optional and alldiscovered entities are ranked.

The system may then rank and cluster the discovered entities (555). Therank assigned to a discovered entity may be based on frequency, forexample how often and how long an entity is on screen. How long anentity is on screen can be determined using the window of capturedscreen images—and is thus a signal unique to the mobile environment.When an entity is always on screen at the same position, the system mayrank the entity low. For example, the map entity mention 410 of FIG. 4is always on screen. So although a map may be car-related, this mapentity mention is not particularly relevant to the main content in thewindow. However, entities that are on screen but not always at the sameposition may be given a high rank. Furthermore, if the window includes alarge quantity of mentions for the same entity, the entity may be givena higher rank. Furthermore, the system may use historical data todetermine if the entity has been seen across multiple mobileapplications. If so, the system may boost the rank for the entity as itis a strong indication the entity is relevant. For example, if a userbooks a flight to Hawaii, makes a hotel reservation to Hawaii, and isnow looking at national parks in Hawaii, the entity for Hawaii may havea high ranking for this time period. Ranking may also account forpositioning on the screen. For example, entities that occur in titlesmay have a higher rank that entities found in the text of a paragraphunder the title or a footnote. In some implementations, the system mayuse the mobile application that generated the content or a user model torank and cluster the entities. For example, entities that appear in thecontent of a particular mobile application may receive a boost, orentities that appear in the content of another mobile application may bedemoted. In some implementations, a user model may determine or specifywhich mobile applications receive a boost. The system may also clusterthe discovered entities and calculate a rank for an entity based on thecluster or collection.

The system may store the discovered entities and the associated ranks.In some implementations, the discovered entities and rank may be storedin an index, such as the screen capture index 119 of FIG. 1. In someimplementations, the rank may be stored in the ranked entities andcollections 117. These discovered entities and ranks may be used toprovide a more personalized user experience, as explained in more detailbelow. The system may perform process 500 continually while screencapture images are received, so that the data stores of discovered andranked entities and indexed screen capture images may be continuouslyupdated.

Providing Actions for Mobile On-Screen Content

Some implementations may identify actionable content in the onscreencontent of a mobile device and provide default actions for theactionable content. Actionable content may include discovered entitiesand landmarks and data that fits a template, such as phone numbers,email addresses, street addresses, dates, etc. Each type of actionablecontent may be associated with a default action. The system may generateannotation data that provides a visual cue for each actionable item.When a user selects the visual cue the system may initiate the defaultaction. The system may identify actionable content across allapplications used on a mobile device, making the user experienceconsistent. For example, while some mobile applications turn phonenumbers into links that can be selected and called, other mobileapplications do not. The annotation data generated by the systemprovides the same functionality across mobile applications.

FIG. 6A illustrates an example display 600 of a mobile computing device.A mobile content context system, such as system 100 of FIG. 1 or system300 of FIG. 3, may capture the display 600 in an image, performrecognition on the image, and find areas of actionable content. Thesystem may then provide annotation data that can be displayed with thecurrent screen. FIG. 6B illustrates the example display of FIG. 6A withannotation data identifying actionable content, in accordance withdisclosed implementations. In the display 600′ of FIG. 6B the annotationdata provides a visual cue 625 for the entity Palo Alto, a visual cue605 for the entity Mio Ristorante Italiano, a visual cue 610 for a website, a visual cue 615 for a street address, and a visual cue 620 for aphone number. In some implementations the visual cues may differ foreach type of actionable content. Each visual cue may be selectable, forexample via a touch, and, when selected, may initiate a default actionassociated with the particular cue. In some implementations when thereare two or more possible actions, the system may allow the user toselect the default action to perform.

FIG. 7 illustrates another example display 700 of a mobile computingdevice with annotation data identifying actionable content, inaccordance with disclosed implementations. In the display 700 theannotation data provides visual cues for several entities and two datesin the display. For example, the display 700 with annotation dataprovides a visual cue 710 for the entity SBC (e.g., State BroadcastingSystem), a visual cue 707 for the YouTube logo, a visual cue 717 for theentity Lady Gaga, and a visual cue 720 for the date “3 November.” Eachvisual cue may represent an area that is selectable by the user of themobile device to initiate an action. For example, if the user selectsthe visual cue 717, the system may open a WIKIPEDIA page about LadyGaga. As another example, if the user selects the visual cue 720 thesystem may open a calendar application to that date.

FIG. 8 illustrates a flow diagram of an example process 800 forgenerating annotation data for actionable content displayed on a mobilecomputing device, in accordance with disclosed implementations. Process800 may be performed by a mobile content context system, such as system100 of FIG. 1 or system 300 of FIG. 3. Process 800 may be used toidentify areas of actionable content in a screen capture image from amobile device and generate annotation data that highlights or otherwisedifferentiates the area of actionable content and provides a defaultaction for the content. Process 800 may begin by receiving an image of ascreen captured on the mobile device (805). The captured image may beobtained using conventional techniques. The system may identifyrecognized items by performing recognition on the image of the capturedscreen (810). Recognized items may be text characters or numbers,landmarks, logos, etc. locating using various recognition techniques,including character recognition, image recognition, logo recognition,etc. Thus, recognized items may include words as well as locations,landmarks, logos, etc. In some implementations steps 805 and 810 may beperformed as part of another process, for example the entity detectionprocess described in FIG. 5.

The system may locate areas of actionable content in the screen captureimage (815). The system may use templates to locate the content. Forexample, a phone number template may be used to find phone numbers intext recognized during the recognition. Similarly, an email template maybe used to find email addresses, a street address template may be usedto locate street addresses, a website template may be used to findwebsites, etc. Each template may represent a different type ofactionable content. In addition to text that matches templates, an areaof actionable content may also be any content determined to correspondto an entity. Entity detection may be performed, for example, by process500. Thus, process 800 may use determined entities and/or candidateentities when looking for actionable content. An entity type is anothertype of actionable content and an entity type may have one or moreassociated default actions. For example, a movie entity may haveassociated actions such as “review the movie,” “buy tickets,” etc.

The system may select some of the identified areas of actionable contentfor use in annotation data (820). For example, the system may determinethat too many areas have been identified and generating visual cues forevery identified area of actionable content may make the displayunreadable and distracting. This may occur, for example, where thesystem identifies many entities in the screen capture image.Accordingly, the system may select the most important or most relevantareas of actionable content to be included in the annotation data. Insome implementations, the system may keep all areas of actionablecontent that are not entities and may use the rank of the identifiedentities to determine which areas to use as areas of actionable content.Whether too many actionable content items have been identified may bebased on the amount of text on the screen. For example, as a user zoomsin, the amount of text and the spacing of the text grows, and actionablecontent items that were not selected when the text was normal size in afirst captured screen image may be selected as the user zooms in, with asecond captured screen image representing the larger text.

Each type of actionable content may be associated with a default action,for example in a data store such as default actions 115 of FIG. 1 ordefault actions 358 of FIG. 3. Accordingly, the system may identify adefault action for each area of actionable content (825) based on thetype of an actionable content item. For example, a street address itemmay open a map mobile application to the address represented by theactionable content item. As another example, a web addresses item mayopen a browser mobile application to the web address, similar to ahyperlink. While some mobile applications offer a phone number orphysical address as a hyperlink, some do not, which makes the userexperience less consistent. Furthermore, in mobile applications that dooffer a phone number or physical address as a hyperlink, the triggeredresponse is often not predictable. For example, one mobile applicationmay open a first map mobile application while another may open a browserapplication or a second map mobile application. Because process 800works across all mobile applications the user is provided a consistentuser interface across all mobile applications with regard to actionablecontent. Other examples of default actions include opening a contactsmobile application for an email address or phone number, initiating aphone call for a phone number, sending an email to an email address,adding an event or reminder in a calendar for a date, etc. In someimplementations, the system may identify two actions for a type, e.g.,adding a contact and sending an email. Thus, an area of actionablecontent may have more than one default action.

The system may generate annotation data with a visual cue for each ofthe areas of actionable content identified (830). The visual cue may beany type of highlighting, outlining, shading, underlining, coloring,etc. that identifies the region of the screen capture image thatrepresents an actionable item. In some implementations, the visual cuemay include an icon, such as a button or down arrow, near the actionablecontent. In some implementations, the system may have a different visualcue for each type of actionable item. For example, entities may behighlighted in a first color, phone numbers in a second color, websitesmay be underlined in a third color, email addresses may be underlined ina fourth color, street addresses may be circled, etc. In someimplementations the user of the mobile device may customize the visualcues. Each visual cue is selectable, meaning that if the user of themobile device touches the screen above the visual cue, the mobile devicewill receive a selection input which triggers or initiates the actionassociated with the visual cue. For example, if the user touches thescreen above the visual cue 707 of FIG. 7, the system may open aWIKIPEDIA page that pertains to the entity YouTube. If the selectedvisual cue is for an actionable content item that has two actions, thesystem may prompt the user of the mobile device to select an action. Forexample, if the user selects the visual cue 620 of FIG. 6, the systemmay provide the user with an opportunity to select making a call to thephone number or adding a new contact.

Each visual cue in the annotation data may have coordinates thatindicate where on the screen the visual cue is located. In someimplementations, each visual cue may also have the image data of thecaptured screen image that corresponds to the coordinates and size ofthe visual cue. In other words, the visual cue may include a portion ofthe screen capture image that corresponds to the visual cue. In someimplementations, the mobile device may have access to the screen captureimage the annotation data was generated for and may not need toassociate the image data with the visual cue, as the system candetermine the image data from the screen capture image using thecoordinates of the visual cue. In another implementation, the system maystore one portion of the screen capture image and its coordinates as areference point. The coordinates and portion of the screen capture imagemay help the system determine whether or not to display the annotationdata with a current screen. If a server generates the annotation data,the server may provide the annotation data to the mobile device.

At the mobile device, the system may determine whether the annotationdata matches the current screen (835). For example, if the mobileapplication currently running (e.g., the mobile application that isgenerating the current screen) is different from the mobile applicationthat generated the screen capture image, the system may determine theannotation data does not match the current screen. As another example,the system may use the screen coordinates or partial image data for atleast some of the visual cues in the annotation data to determine if thecurrently displayed screen is similar to the screen capture image forwhich the annotation data was generated. For example, the system maymatch the image portion that corresponds with a visual cue with the sameportion, using screen coordinates, of the current screen. If the imagedata for that portion does not match, the system may determine that theannotation data does not match the current screen. As another example,the annotation data may include a fiducial mark, e.g., one portion ofthe screen capture image used to generate the annotation data and thesystem may only compare the fiducial mark with the corresponding portionof current screen. In either case, if the user has scrolled, zoomed in,or zoomed out, the current screen may not match the annotation data. Insome implementations, the system may look for the reference point or theportion of the image close by and may shift the display of theannotation data accordingly. In such a situation the system maydetermine that the current screen and the annotation data do match.

If the annotation data and the current screen match (835, Yes), thesystem may display the annotation data with the current screen (840). Ifthe annotation data and the current screen do not match (835, No), thesystem may not display the annotation data with the current screen andprocess 800 ends for the screen capture image. Of course, the system mayperform process 800 at intervals, e.g., each time a screen capture imageis generated. As indicated earlier, process 800 can provide a consistentuser-interaction experience across all mobile applications running onthe mobile device, so that similar types of actionable content act thesame regardless of the mobile application that produced the content. Ofcourse, a user may choose to turn the screen capture feature off, whichprevents process 800 from running. In some implementations, the user mayalso choose to turn off the visual cues generated by process 800, orvisual cues associated with a specific type of actionable content.

It is noted here, yet also applicable to various of the embodimentsdescribed herein, that capabilities may be provided to determine whetherprovision of annotation data (and/or functionality) is consistent withrights of use of content, layout, functionality or other aspects of theimage being displayed on the device screen, and setting capabilitiesaccordingly. For example, settings may be provided that limit content orfunctional annotation where doing so could be in contravention of termsof service, content license, or other limitations on use. Such settingsmay be manually or automatically made, such as by a user whenestablishing a new service or device use permissions, or by an appinstallation routine or the like.

Identifying Relevant Mobile On-Screen Content

Some implementations may identify content on a mobile display that isimportant or relevant to the user of the mobile device. Content may beimportant or relevant because it summarizes a body of text or because itranks highly with regard to user preferences. For example, the systemmay identify entities of interest based on a user profile, which caninclude interests specifically specified by the user or entities andcollections of entities determined relevant to the user based on pastinteractions with mobile applications, e.g., ranked entities andcollections 117 of FIG. 1. When the system identifies a relevant entityin a structure element, e.g., one of a number of entries in a list, thesystem may include the entire structural element as relevant content.For example, the system may generate a visual cue in annotation datathat calls-out the entire list entry. The system may also recognize abody of text in the image and use conventional summarization algorithmsto identify elements of the text that effectively summarize the body oftext. The elements that summarize the body are considered important orrelevant content and may be highlighted or otherwise differentiated fromother screen content using a visual cue in the annotation data.

FIG. 9 illustrates an example display 900 of a mobile computing devicewith annotation data identifying user-relevant content, in accordancewith disclosed implementations. A mobile content context system, such assystem 100 of FIG. 1 or system 300 of FIG. 3, may generate annotationdata that is displayed with a current screen to produce the visual cue905 on the current screen. The visual cue 905 may call the user'sattention to a particular structure element that includes at least oneentity highly relevant to the user. A structure element may be an entryin a list, a cell or row in a table, or some similar display structurethat repeats. Calling out user-relevant content via a visual cue maypersonalize a display of the data. For example, if a person likesItalian food, the system may generate a visual cue for an Italianrestaurant listed in a list of nearby restaurants. In other words, thevisual cue 905 may assist the user in finding a list item, table row,etc., that is most likely interesting to the user of the mobile device.The annotation data may also include other visual cues, such as visualcue 910 that represents actionable content, as described herein.

FIG. 10 illustrates an example display 1000 of a mobile computing devicewith annotation data identifying content-relevant content, in accordancewith disclosed implementations. In the display 1000 the annotation dataprovides a visual cue 1005 for an area of the image that summarizes abody of text. A mobile content context system, such as system 100 ofFIG. 1 or system 300 of FIG. 3, may generate the visual cue 1005 afteranalyzing the content of a screen capture image of the display and usingconventional summarization techniques. For example, content-relevantcontent summarizes the onscreen content and may be one sentence or aparagraph. Calling out such content-relevant summaries may make itquicker and easier for a user to scan through or read a news article,message, document, or other body of text. The annotation data may alsoinclude other visual cues, such as visual cues 1010 and 1015 thatrepresent actionable content, as described herein.

FIG. 11 illustrates a flow diagram of an example process 1100 forgenerating annotation data identifying relevant content in the displayof a mobile computing device, in accordance with disclosedimplementations. Process 1100 may be performed by a mobile contentcontext system, such as system 100 of FIG. 1 or system 300 of FIG. 3.Process 1100 may be used to identify content on a mobile screen that iseither content-relevant or user-relevant, which may make it easier for auser to scan the onscreen content. The system may generate annotationdata that highlights or otherwise differentiates the content-relevant oruser-relevant content from the rest of the display. Process 1100 maybegin by receiving an image of a screen captured on the mobile device(1105). The captured image may be obtained using conventionaltechniques. The system may identify recognized items by performingrecognition on the image of the captured screen (1110). Recognized itemsmay be text characters or numbers, landmarks, logos, etc. identifiedusing various recognition techniques, including character recognition,image recognition, logo recognition, etc. Thus, recognized items mayinclude words as well as locations, landmarks, logos, etc. In someimplementations steps 1105 and 1110 may be performed as part of anotherprocess, for example the entity detection process described in FIG. 5 orthe actionable content process described with regard to FIG. 8.

The system may determine whether the recognized items include a body oftext (1115). The system may determine that the screen capture imageincludes a body of text when the character recognition identifies one ormore paragraphs or when a percentage of the screen capture image thatincludes text is greater than 50%. In some implementations, the systemmay determine that the body of text includes a minimum number of words.The system may consider each paragraph a separate body of text, or thesystem may considered a continuous block of text, for example when theparagraphs relate to the same topic. For example the system candetermine if two paragraphs refer to the same entities, or a have aminimum number of entities in common. If the system finds a body of textin the image (1115, Yes), the system may analyze the text usingconventional summarization techniques to determine a portion of the bodythat serves as a summary (1120). The portion may be a sentence or aparagraph, or some other portion of the text. The system may generateannotation data that includes a visual cue that differentiates thesummary portion from surrounding content (1125). As previouslymentioned, the visual cue may be any kind of marking that differentiatesthe summary portion from the other content of the mobile screen. Thevisual cue may include or be associated with metadata, such as screencoordinates, an image portion, etc. as described herein. The summaryportion is considered content-relevant because it summarizes the contentof the screen capture image.

The system may also look for entities mentioned in the recognizedcontent (1130). This may be performed as discussed above with regard toFIG. 5. In some implementations, the system may use candidate entitiesidentified in the screen capture image. The system may also determinewhether the content of the screen capture image includes structureelements (1135). Structure elements may represent any kind of repeatingdisplay item, such as list entries, table rows, table cells, searchresults, etc. If the content includes a structure element (1135, Yes),the system may determine if there is a structure element associated witha highly ranked entity (1140) or with a number of such entities. Anentity may be highly ranked based on a user profile, device profile,general popularity, or other metric. The profile may include areas ofinterest specified by the user and entities or collections determined tobe particularly relevant to a user based on historical activity. Forexample, the system may use a data store of ranked entities andcollections, such as ranked entities and collections 117 of FIG. 1 orranked entities 357 of FIG. 3. In particular, if an entity is a memberof a collection that is of interest to the user, for example Italianrestaurants, the system may boost a rank for the entity, even if theentity does not have a high rank with regard to the image or recentactivity. In some implementations, the system may identify more than oneentity in a structure element and calculate an aggregated rank for theentities found in the structure element. The system may compare theaggregated rank to a rank threshold and, when the aggregated rank meetsthe threshold the entities may be considered highly ranked. In someimplementations, entities with a rank that exceeds a rank threshold maybe considered highly ranked. When a highly ranked entity is associatedwith a structure element, the system may generate annotation data with avisual cue that differentiates the structure element (1145). The systemmay generate a visual cue for each structure element that includes ahighly relevant entity. In some implementations, if the systemidentifies too many highly relevant entities the system may adjust therank threshold to eliminate some of the entities considered highlyranked or select a predetermined number of the highest ranked entities,thereby decreasing the number of visual cues that correspond touser-relevant items. In an implementation where a server generates theannotation data, the server may provide the annotation data to themobile device.

At the mobile device, the system may determine whether the annotationdata matches the current screen (1150). For example, if the mobileapplication currently running (e.g., the mobile application that isgenerating the current screen) is different from the mobile applicationthat generated the screen capture image, the system may determine theannotation data does not match the current screen. As another example,the system may use the screen coordinates or partial image data for atleast some of the visual cues in the annotation data to determine if thecurrently displayed screen is similar to the screen capture image forwhich the annotation data was generated. For example, the system maymatch the image portion that corresponds with a visual cue with the sameportion, using screen coordinates, of the current screen. If the imagedata for that portion does not match, the system may determine that theannotation data does not match the current screen. As another example,the annotation data may include a reference point, e.g., one portion ofthe screen capture image used to generate the overlay and the system mayonly compare the reference point with the current screen. In eithercase, if the user has scrolled, zoomed in, or zoomed out, the currentscreen may not match the annotation data. In some implementations, thesystem may look for the reference point or the portion of the imageclose by and may shift the display of the annotation data, in scale orposition, accordingly. In such a situation the system may determine thatthe current screen and the annotation data do match.

If the annotation data and the current screen match (1150, Yes), thesystem may display the annotation data with the current screen (1155).If the annotation data and the current screen do not match (1150, No),the system may not display the annotation data with the current screen.Process 1100 ends for the screen capture image, although the system mayperform process 1100 at intervals, e.g., each time a screen captureimage is generated. As indicated earlier, process 1100 can provide aconsistent user-interaction experience across all mobile applicationsrunning on the mobile device, so that user-relevant or content-relevantitems are called out regardless of the mobile application that producedthe content. Of course, a user may choose to turn the screen capturefeature off, which prevents process 1100 from running. In someimplementations, the user may also be provided the opportunity to turnon and off the visual cues generated by process 1100.

Providing Insight for Entities in Mobile On-Screen Content

Some implementations may identify entities in a screen displayed on amobile device and provide an interface for surfacing information aboutthe entities. The interface provides a powerful way of answering queriesabout an entity without leaving the context of the mobile application.The interface may be combined with, for example, the actionable contentinterface described earlier, with a different input triggering theinsight interface. For example, a visual cue generated for an entity maybe actionable to initiate a default action when the entity is selectedwith a short tap and may be actionable to initiate a process thatprovides insight on the connection(s) of the entity to other entities onthe screen with a long press, or press-and-hold action, or a two-fingerselection, etc. The second input need only be different from the firstinput that triggers the default action. The second input can be referredto as an insight selection. If the user performs an insight selection onone entity, the system may traverse a data graph to find other entitiesrelated to the selected entity in the graph that also appear on thescreen. If any are found, the system may provide annotation data thatshows the connections. A user can select the connection to see adescription of the connection. If a user performs an insight selectionon two entities at the same time, the system may walk the data graph todetermine a relationship between the two entities, if one exists, andprovide annotation data that explains the connection. In someimplementations, the system may initiate a cross-application insightmode, for example when a user performs a second insight selection of anentity. The cross-application insight mode may cause the system tosearch for previously captured images that include entities related tothe selected entity. Any previously captured images with an entityrelated to the selected entity may be provided to the user, similar to asearch result. In some implementations, the system may provide theimages in a film-strip style user interface or other scrollable userinterface.

FIG. 12A illustrates an example display 1200 of a mobile computingdevice screen with annotation data highlighting connections betweenentities found in the content displayed on a mobile device, inaccordance with disclosed implementations. A mobile content contextsystem, such as system 100 of FIG. 1 or system 300 of FIG. 3, maygenerate annotation data that is displayed with a current screen on amobile device to produce the display 1200. The display 1200 may includevisual cues 1210 and 1220 that indicate entities related to the entityrepresented by the visual cue 1205. The entities themselves may have avisual cue, such as visual cue 1205 and visual cue 1225. In someimplementations, the visual cue showing a relationship may be a linelinking the entities. In some implementations, the line may be labeledwith a description of the relationship between the two entities, such asvisual cue 1210. In some implementations the line may not be labeled,such as visual cue 1220. The visual cue showing the relationship mayalso include an indication of relatedness. For example, a line betweentwo actors who co-starred in one movie may be thinner or a differentcolor or pattern from the line between two actors who co-starred inseveral movies. Visual cues may provide functionality, such a hyperlinkto a source discussing the relationship between the entities. Of course,the visual cue representing the relationship is not limited to a lineand may include changing the appearance of the visual cues for entitiesthat are related, etc. In some implementations, the system may generatethe visual cue 1210 and 1220 in response to an insight selection of thevisual cue 1205.

FIG. 12B illustrates an example display 1200′ of a mobile computingdevice displayed with annotation data providing information about aconnection between two entities found in the content displayed on amobile device, in accordance with disclosed implementations. A mobilecontent context system, such as system 100 of FIG. 1 or system 300 ofFIG. 3, may generate annotation data that is displayed with a currentscreen on a mobile device to produce the display 1200′. The display 1200may include annotation data that includes visual cue 1250 andexplanation 1255 to be displayed with the current screen. The display1200′ may thus represent the same current screen as display 1200 in FIG.12A, but different annotation data based on a different insightselection. For example, the system may generate the annotation data usedto produce display 1200′ when a user selects both the Marshall Islandsentity and the Majuro entity at the same time. As a result, the systemmay determine the relationship between these two entities in a datagraph and provide an explanation of the relationship as explanation1255. The annotation data used to generate display 1200′ may of coursealso include other visual cues, such as visual cues in addition tovisual cue 1250 and explanation 1255.

FIGS. 13A-B illustrate a flow diagram of an example process 1300 forgenerating annotation data identifying insightful connections betweenentities found in the content displayed on a mobile device content inthe display of a mobile computing device, in accordance with disclosedimplementations. Process 1300 may be performed by a mobile contentcontext system, such as system 100 of FIG. 1 or system 300 of FIG. 3.Process 1300 may be used to provide insight about relationships withonscreen entities without leaving the context of the currentapplication. In this manner, process 1300 may provide answers to queriesusing annotation data displayed along with the current screen generatedby the mobile application. Process 1300 may begin when the systemreceives an insight selection of a first entity that is identified in afirst annotation data for a mobile device (1305). For example, thesystem may have generated the first annotation data as a result ofprocess 800 described above with regard to FIG. 8. The first annotationdata may thus have visual cues for actionable content. The visual cuesassociated with entity types of actionable content may be configured toreact to two types of input, one that initiates a default action andanother that initiates entity insight surfacing. In some implementationsthe insight selection may be a long press or a press-and-hold type ofaction.

The system may determine entities related to the first entity in a datagraph (1310). For example, in some implementations the system may walkthe data graph, such as data graph 190, from the first entity to eachentity it is connected to within a specified path length. In someimplementations the path length may be one or two. In other words, thesystem may consider entities related to the first entity if the entitiesare directly related, or related through one intermediate entity, to thefirst entity. Entities reached via the paths within the specified pathlength may be considered related entities. The system may then identifya second entity that is a related entity and is associated with thescreen capture image that corresponds with the first annotation data(1315). The system may identify more than one entity that is a relatedentity and also associated with the screen capture image. The system maygenerate second annotation data, the second annotation data including avisual element linking the first entity with the second entity (1320).The second annotation data may include the first annotation data or maybe displayed with the first annotation data. In some implementations thevisual element may be a line connecting the first entity and the secondentity. If the system identifies more than one entity related to thefirst entity, the system may generate one visual element for each entityfound. Thus, for example, in FIG. 12A the system generated visualelement 1210 and visual element 1220. The system may display the secondannotation data with the current screen (1325). This may occur in themanner described above with regard to FIGS. 8 and 11. Accordingly, ifthe second annotation data does not match the current screen, process1300 may end, as the user has left the screen that corresponds with thesecond annotation data. If the second annotation data does not includethe first annotation data, step 1325 may include displaying the firstannotation data and the second annotation data with the current screen.

The system may determine if a selection of one of the visual elementsrepresenting the link has been received (1330). The selection may be atouch of the line that connects the two entities, for example. If aselection of the visual element has been received (1330, Yes), thesystem may generate third annotation data (1340). The third annotationdata may include a text area describing the relationship between thefirst entity and the second entity in the graph-based data store. Thetext area may be a label added to the visual element, such as visualelement 1210 of FIG. 12A or may be an explanation box, such asexplanation 1255 of FIG. 12B. The third annotation data may include thesecond annotation data and the first annotation data or may beconfigured to be displayed with the first annotation data and the secondannotation data. The mobile device may display the third annotation datawith a current screen on the mobile device (1345). This may occur in themanner described above with regard to step 1325.

If a selection of the visual element has not occurred (1330, No), thesystem may check for a cross-application selection (1350). Across-application selection may be a second insight selection for thesame entity. For example, if the user performs a long press on an entityand the system provides visual elements linking that entity to otherentities, and the user performs another long press on the same entity,the second long press may be considered a cross-application selection.

When the system receives a cross-application selection (1350, Yes), thesystem may identify a plurality of previously captured images associatedwith the related entities (1355 of FIG. 13B). The related entities mayhave been determined as part of step 1310 or the system may determinerelated entities again. In some implementations, the system may issue aquery against an index of previously captured images, the queryincluding each of the related entities. In some implementations, thesystem may select the most highly ranked related entities and use thesein the query. The system may use the previously captured screens thatare provided as a search result to generate a user interface fordisplaying the plurality of previously captured images (1360). In someimplementations, the user interface may be provided as annotation data.In some implementations, the system may switch the mobile application toa search application that displays the search result, for example, as ascrollable film-strip or some other array of images.

Process 1300 can provide a method of making information in the datagraph accessible and available in a consistent way across all mobileapplications. This allows a user to query the data graph without leavingthe context of the application they are currently in. Such insight canhelp the user better understand onscreen content and more easily findanswers to questions.

FIG. 14 illustrates a flow diagram of an example process 1400 forgenerating annotation data providing information on a connection betweenentities found in the content displayed on a mobile device content inthe display of a mobile computing device, in accordance with disclosedimplementations. Process 1400 may be performed by a mobile contentcontext system, such as system 100 of FIG. 1 or system 300 of FIG. 3.Process 1400 may also be used to provide insight about relationshipswith onscreen entities without leaving the context of the currentapplication. In this manner, process 1400 may provide answers to queriesusing annotation data displayed along with the current screen generatedby the mobile application. Process 1400 may begin when the systemreceives an insight selection of a first entity and a second entity(1405). The first entity and the second entity may be identified viavisual cues in a first annotation data for a mobile device. The systemmay then determine the relationships that connect the first entity tothe second entity in the data graph (1410). In some implementations, thesystem may walk paths from the first entity to the second entity. Insome implementations, the walks may be limited by a path length, forexample two or three. The system may generate second annotation data,the second annotation data including a text area that describes therelationship between the first entity and the second entity in thegraph-based data store (1415). For example, the system may base the texton the labeled edges in the data graph that connect the two entities.The system may display the second annotation data with a current screenon the mobile device (1420), as explained above with regard to FIGS. 8,11, and 13A.

Indexing Mobile OnScreen Content

Some implementations may identify content on a screen of a mobile deviceand may index the content in a way that allows the content to besearched and recalled at a later time. The system may identify key itemsin a screen capture image and generate an index that matches the keyitems to the screen capture image. Key items may be words, phrases,entities, landmarks, logos, etc., discovered via recognition performedon the image. The index may be an inverted index that, for each keyitem, includes a list of images associated with the key item. In someimplementations, any annotation data generated for an image may also bestored with the image. The system may rank key items using conventionalsignals as well as signals unique to the mobile environment.

The system may query the index by searching for key items responsive tothe query. In some implementations, the system may generate annotationdata for responsive data that includes a visual cue for content that isresponsive to the query, helping the user to see why the previouslycaptured image was responsive. In some implementations, the system mayprovide only a portion of the previously captured image, e.g., asnippet, that includes the responsive content. The snippet may includean area around the responsive key item in the image. The search resultmay be a scrollable list, such as a carousel of images, a grid ofimages, a film-strip style list, etc. The system may use conventionalnatural language processing techniques to respond to natural languagequeries, whether typed or spoken. The system may use signals unique tothe mobile environment to generate better search results. For example,some verbs provided in the query may be associated with certain types ofmobile applications and images captured from those mobile applicationsmay receive a higher ranking in generating the search results. Forexample, the verb “mention” and similar verbs may be associated withcommunications applications, such as chat and mail applications. Whenthe system receives a query that includes the verb “mention” the systemmay boost the ranking of responsive content found in images associatedwith the communications applications. Selecting a search result maydisplay the search result with associated annotation data, if any, ormay take the user to the application and, optionally, to the placewithin the application that the selected search result image was takenfrom.

FIG. 15 illustrates a flow diagram of an example process 1500 forgenerating an index of screen capture images taken at a mobile device,in accordance with disclosed implementations. Process 1500 may beperformed by a mobile content context system, such as system 100 of FIG.1 or system 300 of FIG. 3. Process 1500 may be used to generate an indexthat makes previously captured screen images searchable, so that theuser can retrieve the user's previously viewed content. Process 1500 maybegin when the system receives an image of a screen captured at a mobiledevice (1505). The captured image may be obtained using conventionaltechniques. The system may identify recognized items by performingrecognition on the image of the captured screen (1510). Recognized itemsmay be text characters or numbers, landmarks, logos, etc. identifiedusing various recognition techniques, including character recognition,image recognition, logo recognition, etc. Thus, recognized items mayinclude words as well as locations, landmarks, logos, etc. In someimplementations steps 1505 and 1510 may be performed as part of anotherprocess, for example the entity detection process described in FIG. 5,the actionable content process described with regard to FIG. 8, or therelevant content process described with regard to FIG. 11.

The system may index key items identified by the recognition (1515). Forexample, the system may identify words and phrases from textrecognition, may identify entities from text recognition, imagerecognition, and logo recognition, landmarks from image recognition,etc. In some implementations, the entities may be candidate entities anddiscovered entities identified during process 500, described above. Thewords, phrases, entities, and landmarks are examples of key items. Thesystem may associate the image with each of the key items identified inthe image using the index. In some implementations, the index may be aninverted index, so that each key item has an associated list of imagesin which the key item was found. In addition, the system may associatemetadata with the image and key item. For example, the metadata mayinclude where in the image the key item occurs, the rank of the key itemwith regard to the image, a timestamp for the image, a geo location ofthe device when the image was captured, etc. Accordingly, the system maycalculate a rank for the key item with regard to the image and store therank with the image and key item in the index. (1520). The rank of a keyitem may be calculated using conventional ranking techniques as well aswith additional signals unique to the mobile environment. For example,when a key item is static across each image captured for a particularapplication, the system may rank the key item very low with regard tothe image, as the key item occurs in boilerplate and is likely not veryrelevant to the user or the user's activities. Examples of boilerplateinclude item 710 of FIG. 7 and items 410 of FIG. 4. In someimplementations, key items located in areas of the screen that do notchange for a particular mobile application may be eliminated from theindex. In some implementations, ranking may be similar to or updated bythe rank calculated by process 500 of FIG. 5.

The system may store the index in a memory (1525). In someimplementations, the user may specify the location of the stored index,such as on the mobile device or at a server that includes a profile forthe user. In some implementations, the index may store screen captureimages and key items from more than one device operated by the user. Insome implementations, the index may include the screen capture image,and in some implementations the screen capture image may be stored in aseparate data store or table. In some implementations, annotation datagenerated for the image may be stored with the image and may bedisplayed with the image after selection of the image from a searchresult. Process 1500 ends for this image, but the system may repeatprocess 1500 each time a screen capture image is generated by the mobiledevice. Of course, a user may choose to turn the screen capture featureoff, which prevents process 1500 from running.

FIG. 16 illustrates a flow diagram of an example process 1600 forquerying an index of screen capture images taken at a mobile device, inaccordance with disclosed implementations. Process 1600 may be performedby a mobile content context system, such as system 100 of FIG. 1 orsystem 300 of FIG. 3. Process 1600 may be used to search an index ofpreviously captured screen images that were captured on a user's mobiledevice. A search result for a query may include one or more of thepreviously captured screen images or portions of the images that includekey items responsive to the query. The search result may rank theresponsive previously captured screen images (or the portions) withregard to the query, so that higher ranking screen capture images appearfirst in the search results. The system may use ranking signals uniqueto the mobile environment to determine the rank of a responsive screencapture image. In some implementations, the system may associate certainverbs with a type or class of mobile application. For example, the verbs“say” and “mention” may be associated with communication applications,such as messaging and email applications. Likewise, the verbs “watch”and “view” may be associated with video applications, such as YouTube,FaceTime, Netflix, etc., When a user enters a natural language query,the system may boost the rank of a responsive image that matches thetype associated with the verbs.

Process 1600 may begin when the system receives a query (1605). Thequery can be a natural language query or a query that includes other keyitems. In some implementations, the query may be submitted via a searchmobile application on the mobile device by the user. In someimplementations, the query may be submitted by the system to helpgenerate annotation data, as will be explained in further detail herein.The system may use conventional natural language processing techniquesand query parsing techniques to determine what key items are associatedwith the query. The system may use the key items associated with thequery to search the index for screen capture images responsive to thequery (1610). Screen capture images captured from the user's mobiledevice that are associated with key items associated with the query maybe considered responsive images. For each responsive image, the systemmay generate search result annotation data (1615). In someimplementations, the search result annotation data may generate a visualcue for each area of the image that corresponds with a responsive keyitem. In some implementations, the search result annotation data maymake the image (or the image portion) an area of actionable content,where the action associated with the actionable content opens the mobileapplication that generated the screen captured in the image and mayoptionally take the user to the place or state in the mobile applicationrepresented by the image.

The system may provide at least a portion of each responsive image as asearch result (1620). In some implementations, the portion may be athumbnail size image with the annotation data that includes a visual cuefor responsive key items. In some implementations, the portion may be aportion of the image that includes the responsive key item, so that thesystem displays a responsive snippet from the original image. In someimplementations, the portion may be the whole image. In someimplementations, the system may present the search results, which caninclude a plurality of previously captured images, in a scrollable list,such as a film-strip, a carousel, a scrollable grid, etc.

The user may select one of the images from the search results, and thesystem may receive the selection (1625). If the selected image was anarea of actionable content with a default action (1630 Yes), the systemmay launch the mobile application associated with the selected image asthe default action (1635). If the selection did not involve anactionable item (1630, No), the system may determine whether annotationdata associated with the selected image exists (1640). The annotationdata may have been generated, for example, as part of determiningactionable content or relevant content for the image. In someimplementation, the annotation data may be associated with the imageafter it is generated, for example in the index or screen capture data.If the annotation data exists (1640, Yes), the system may apply theannotation data to the selected image (1645). The system may provide theselected image for display on the screen of the mobile device (1650).For example, when the user selects a search result (e.g., the thumbnailor portion of a previously captured image), the system may display thefull image, and any annotation data previously generated for the image,on the display of the mobile device. In some implementations, the usermay perform an action on the displayed image that attempts to return theuser to the state of the mobile device represented by the image. Forexample, the user may perform an action that causes the mobile device toreturn to the mobile application and the place within the mobileapplication represented by the image, as will be explained in furtherdetail herein. Process 1600 then ends, having provided the user with aninterface for searching previously viewed content.

Providing User Assistance from Interaction Understanding

Some implementations may use information on the current screen of themobile device and information from previously captured screen images topredict when a user may need assistance and provide the assistance inannotation data. In one implementation, the system may use key contentfrom a captured image as a query issued to the system. Key contentrepresents the most relevant or important (i.e., highest ranked) keyitems for the image. When a key item for a previously captured screenimage has a rank that meets a relevance threshold with regard to thequery the system may select the portion of the previously capturedscreen image that corresponds to the key item and provide the portion asannotation data for the current screen capture image. In someimplementations, the system may analyze the key items in the currentscreen capture image to determine if the key items suggest an action. Ifso, the system may surface a widget that provides information for theaction. In some implementations, the system may use screen captureimages captured just prior to the current screen capture image toprovide context to identify the key content in the current screencapture image. The system may include a model trained by a machinelearning algorithm to help determine when the current screen suggests anaction, and which type of action is suggested.

FIGS. 17-19 illustrate example displays for a mobile computing devicewith automated assistance from interaction understanding, in accordancewith disclosed implementations. A mobile content context system, such assystem 100 of FIG. 1 or system 300 of FIG. 3, may generate annotationdata that is displayed with a current screen on a mobile device toproduce the displays illustrated. In the example of FIG. 17, the systemhas determined that the current screen includes information thatsuggests looking up a contact (e.g., suggests an action). The system hasprovided annotation data that includes assistance window 1705 to producedisplay 1700. The assistance window 1705 includes information surfacedusing a contact widget. For example, the contact widget may look in thecontacts associated with the mobile device for the person mentioned andprovide the information about the contact.

In the example of FIG. 18, the system has determined that the currentscreen includes information that suggests scheduling an event (e.g.,another type of action). The system has provided annotation data thatincludes assistance window 1805 to produce display 1800. The assistancewindow 1805 includes a calendar widget that adds a new event to thecalendar with the event information, such as date and time, surfacedbased on information found in the screen. Thus, an assistance window maybe configured to perform an action (e.g., adding a new calendar event)as well as displaying information obtained from another mobileapplication (e.g., displaying any existing calendar events for the datementioned). In the example of FIG. 19, the system has determined that apreviously viewed screen, e.g., screen 1950, has information that may behelpful or relevant to the user for the current screen, e.g., screen1900. The system has provided annotation data that includes assistancewindow 1905 to produce display 1900. The assistance window 1905 includesa snippet of the previously viewed screen, indicated by the dashedlines, that includes information highly relevant to the current screen1900. The previously viewed screen 1950 may have been captured andindexed, as discussed herein.

FIG. 20 illustrates a flow diagram of an example process 2000 forgenerating annotation data with an assistance window based oninteraction understanding, in accordance with disclosed implementations.Process 2000 may be performed by a mobile content context system, suchas system 100 of FIG. 1 or system 300 of FIG. 3. Process 2000 may beused to automatically generate an assistance window based on the contextof the current screen. Process 2000 may begin when the system receivesan image of a screen captured at a mobile device (2005). The capturedimage may be obtained using conventional techniques. The system mayidentify recognized items by performing recognition on the image of thecaptured screen (2010). Recognized items may be text characters ornumbers, landmarks, logos, etc. identified using various recognitiontechniques, including character recognition, image recognition, logorecognition, etc. Thus, recognized items may include words as well aslocations, landmarks, logos, etc. In some implementations steps 2005 and2010 may be performed as part of another process, for example the entitydetection process described in FIG. 5, the actionable content processdescribed with regard to FIG. 8, the relevant content process describedwith regard to FIG. 11, or the indexing process described with regard toFIG. 15.

The system may identify key content in the image and use the key contentto query an index of previously captured images (2015). Key content mayinclude key items, e.g., those identified during a indexing process suchas process 1500 of FIG. 15, that have the highest ranks with regard tothe image. In some implementations, the rank may need to exceed athreshold to be considered key content. The system may issue a queryusing the key content, for example using process 1600 described abovewith regard to FIG. 16. When the system receives the search results, thesystem may determine if the search results include a search result witha high confidence match with regard to the query (2020). For example,previously captured screen images that occur close in time to the imagemay be considered more relevant. In addition, previously captured screenimages that were capturing from mobile applications of the same type orclassification (e.g., travel applications) may be considered morerelevant. The system may use a threshold to determine if any of thesearch results include a high enough confidence. If none do, process2000 ends, as the system is not confident that any of the relevantpreviously viewed images would be of assistance to the user.

If at least one search result is a high confidence match (2020, Yes),the system may select a portion of the search result (e.g., the entirepreviously captured screen image, or a snippet of the image thatincludes responsive items) for use in annotation data that includes anassistance window (2025). The snippet may include an area of the imagearound the responsive content. The annotation data may be provided tothe mobile device for display with the currently running application.Accordingly, at the mobile device, the system may determine whether theannotation data matches the current screen (2030). For example, if themobile application currently running (e.g., the mobile application thatis generating the current screen) is different from the mobileapplication that generated the screen capture image (e.g., from step2005), the system may determine the annotation data does not match thecurrent screen. As another example, the annotation data may include areference point, e.g., one portion of the screen capture image used togenerate the annotation data, and the system may compare the referencepoint with the current screen. In either case, if the user has scrolled,zoomed in, or zoomed out, the current screen may not match theannotation data. In some implementations, the system may look for thereference point close by and may shift the display of the annotationdata accordingly. In such a situation the system may determine that thecurrent screen and the annotation data do match.

If the annotation data and the current screen match (2030, Yes), thesystem may display the annotation data with the current screen (2035).If the annotation data and the current screen do not match (2030, No),the system may not display the annotation data with the current screen.Process 2000 ends for the screen capture image, although the system mayperform process 2000 at intervals, e.g., each time a screen captureimage is generated. In some implementations, process 2000 may beperformed in conjunction with other analysis and processes performed ona captured image. Of course, a user may choose to turn the screencapture feature off, which prevents process 2000 from running. In someimplementations, the user may also be provided the opportunity to turnon and off the visual cues generated by process 2000.

FIG. 21 illustrates a flow diagram of another example process 2100 forgenerating annotation data with an assistance window based on contentcaptured from a mobile device, in accordance with disclosedimplementations. Process 2100 may be performed by a mobile contentcontext system, such as system 100 of FIG. 1 or system 300 of FIG. 3.Process 2100 may use a model trained by a machine learning algorithm torecognize actions within the content of a screen capture image and mayprovide a default event action or widget to provide assistance based onthe action. Process 2100 may begin when the system receives an image ofa screen captured at a mobile device (2105) and identifies recognizeditems by performing recognition on the image of the captured screen(2110), as described above. In some implementations steps 2105 and 2110may be performed as part of another process, for example the entitydetection process described in FIG. 5, the actionable content processdescribed with regard to FIG. 8, the relevant content process describedwith regard to FIG. 11, the indexing process described with regard toFIG. 15, or process 2000 described above.

The system may determine whether any action is suggested in therecognized content of the screen capture image (2115). Actions can beany activity that suggests an action to be taken by the user. Forexample, actions may include adding an event for a calendar entry,looking up availability for a certain date, looking up or adding names,numbers, and addresses for a contact, adding items to a to-do list,looking up items in a to-do list, or otherwise interacting with a themobile device. In some implementations, the system may include a machinelearning algorithm that can learn actions commonly performed by the userin the past and predict when it is likely the user intends to performthose actions again. For example, if the user commonly opens twoapplications together, e.g., a crossword application and a dictionaryapplication, the action may be opening the dictionary application whenthe user opens the crossword application. An action element may be thetext that triggers or suggests the action. If no action elements arefound (2115, No), process 2100 ends as no assistance window isgenerated. If an action element is found (2115, Yes), the system maygenerate annotation data with an assistance window for the actionelement (2125). In some implementations, the system may have a datastore that associates an event action with an action element. Forexample, the system may determine if the action element is related to acontacts widget or a calendar widget using, for example, event actions114 of FIG. 1. The assistance window may include information obtainedfrom a data store. For example, the assistance window may query the datastore and provide data from the data store in text format. For example,the system may query contact information for a person mentioned in thecontent of the screen capture image and provide the contact informationin the assistance window. As another example, the system may querycalendar information for the user for a window of time that includes adate and time suggested in the image and provide the schedule of theuser for the window of time in the assistance window. As anotherexample, the system may determine, e.g., using a machine learningalgorithm, that the user is likely to repeat some action previouslyperformed and suggest performing the action. Performing the repeatedaction may include automating user input, as described below. In someimplementations, the assistance window may include a suggestion toautomatically perform an action. For example, the assistance window mayinclude text that describes the action to be performed, such as adding anew contact, and the assistance window may be selectable. When selected,the assistance window may launch the action suggested on the mobiledevice.

The annotation data may be provided to the mobile device for displaywith the current screen. Accordingly, at the mobile device, the systemmay determine whether the annotation data matches the current screen(2130). For example, if the mobile application currently running (e.g.,the mobile application that is generating the current screen) isdifferent from the mobile application that generated the screen captureimage (e.g., from step 2105), the system may determine the annotationdata does not match the current screen. As another example, theannotation data may include a reference point, e.g., one portion of thescreen capture image used to generate the annotation data, and thesystem may compare the reference point with the current screen. Ineither case, if the user has scrolled, zoomed in, or zoomed out, thecurrent screen may not match the annotation data. In someimplementations, the system may look for the reference point close byand may shift the display of the annotation data accordingly. In such asituation the system may determine that the current screen and theannotation data do match.

If the annotation data and the current screen match (2130, Yes), thesystem may display the annotation data with the current screen (2135).If the annotation data and the current screen do not match (2130, No),the system may not display the annotation data with the current screen.Process 2100 ends for the screen capture image, although the system mayperform process 2100 at intervals, e.g., each time a screen captureimage is generated. In some implementations, process 2100 may beperformed in conjunction with other analysis and processes performed ona captured image. Of course, a user may choose to turn the screencapture feature off, which prevents process 2100 from running. In someimplementations, the user may also be provided the opportunity to turnon and off the visual cues generated by process 2100.

Automating User Input from Mobile On-Screen Content

Some implementations may capture user input actions while screen captureimages are captured on a mobile device and use the user input actions toreturn the mobile device to a state represented by a previously capturedscreen image or to automatically perform a task for a user with minimaladditional input. The user input actions include taps, swipes, textinput, etc. performed by a user when interacting with the touch-screenof a mobile device. The system may store the input actions and use themto replay the actions of the user. Replaying the input actions may causethe mobile device to return to a previous state, or may enable themobile device to repeat some task with minimal input. For example, theuser input actions may enable the mobile device reserve a restaurantusing a specific mobile application by receiving the new date and timeusing user input actions used to reserve the restaurant a first time.Returning to a previous state provides the user with the ability todeep-link into a particular mobile application. In some implementations,the mobile device may have an event prediction algorithm, for exampleone used to determine action elements as part of process 2100 of FIG.21, that determines a previously captured image that represents anaction the user will likely repeat.

FIG. 24 illustrates example displays for a mobile computing device forselecting a previously captured image, in accordance with disclosedimplementations. In the example of FIG. 24, display 2400 represents aselectable assistance window 2405 with a preview 2410 of the previouslycaptured image. The previously captured screen image represented bypreview 2410 may be included, for example, in an index of previouslycaptured screen images from the user device. When the user selects theassistance window (or a control for the window, etc.), the system mayautomatically take the mobile device to the state represented by thepreview 2410, using the previously captured screen image as the selectedimage. As one example, the system may use the machine learning algorithmto determine that the user makes a dinner reservation for two at Mr.Calzone most Fridays and generate assistance window 2405 to automate thenext reservation. Display 2450 illustrates an example of a search resultfor previously captured screen images. When the user selects image 2455,the system may endeavor to automatically take the mobile device to thestate represented by the image 2455, as described below. In other words,the system may attempt to open the app that originally generated image2455 and re-create the actions that resulted in image 2455. Of courseimplementations may include other methods of obtaining a previouslycaptured screen image.

FIG. 22 illustrates a flow diagram of an example process 2200 forautomating user input actions based on past content displayed on amobile device, in accordance with disclosed implementations. Process2200 may be performed by a mobile content context system, such as system100 of FIG. 1 or system 300 of FIG. 3. Process 2200 may use previouslycaptured user input data to take the user back to a state represented bya selected image of a previous screen viewed by the user. Process 2200may be an optional process that the user of the mobile device controls.In other words, the user of the mobile device may choose to have userinput actions stored, or the user may turn of storing of user inputactions. When the user turns on the collection of user input actions,the user may have access to the functionality provided by process 2200.

Process 2200 may begin when the system receives a selection of a firstimage that represents a previously captured screen (2205). The selectionmay be from a search result, or may be from a mobile applicationconfigured to allow the user to select a previously captured screen, ormay be a screen selected as a prior action the user wants to repeat, ormay be a screen shared with the user from another mobile device. Thefirst image is associated with a timestamp and a mobile application thatwas executing when the image was captured. The system may then locate asecond image that represents a different previously captured screen(2210). The second image represents a reference screen. The referencescreen may be a home screen for the mobile device (e.g., the screen thatdisplays when the mobile device is turned on) or may be an initialscreen for the mobile application (e.g., the screen that first displayswhen the mobile application is activated from the home screen). Thesecond image also has a timestamp, which is earlier than the timestampof the first image. In other words, the system may look backwards intime through previously captured screen images for an image representinga reference screen. The second image, thus, represents the referencescreen that preceded the first image.

The system may identify a set of stored user inputs occurring betweenthe two timestamps and a set of previously captured screen images thatoccur between the two timestamps (2215). The user inputs may have beencaptured, for example, by a screen capture engine, such as screencapture application 301 of FIG. 3. The system may cause the mobiledevice to begin at the reference screen (2220). In other words, thesystem may take the mobile device to the home screen or may start-up theapplication associated with the first image, as if it were initiatedfrom the home screen, depending on what the reference screen reflects.The system may then begin replaying the user input actions in order,e.g., starting with the earliest user input action in the set (2225).The system may replay the user input actions until the next user inputaction in the set occurs after the timestamp for the next screen captureimage in the set of images. In re-playing the user input actions, thesystem sends a signal to the processor of the mobile device that mimicsthe action and location performed by the user. User input actions withthe same timestamp may be replayed at the same time—e.g., simulating amulti-finger input. The mobile device then responds to the replayedaction as if a user had performed the action. In some implementations,the system may replay the actions using a virtual screen, e.g., one thatis not visible to a user of the mobile device until the replay ends.

After the system replays the user input action that occurred just priorto the next screen capture image in the set of images, the systemcompares the screen displayed on the mobile device with the next screencapture image in the set (2230). Determining whether the screens matchmay be similar to determining whether annotation data matches a currentscreen, as described above. In other words, the system may compareportions of the screen displayed and the next screen capture image, orportions thereof. If the two screens do not match (2230, No), the systemmay stop replaying user input actions and process 2200 ends. This mayoccur because the user input actions no longer lead to the same place inthe application. In other words, the system cannot recapture the state.This may occur for several reasons, one of which is that content hasbeen deleted or moved. Thus, the system will attempt to bring the useras close as possible to the desired state, but may terminate when it isapparent that the path followed using the original user input actionsleads to a different place.

If the screens do match (2230, Yes), the system may determine if thenext image in the set of images is the first image (2235). In otherwords, the system may determine if it has arrived at the desired state.If so (2235, Yes), process 2200 ends. If not (2235, No), the system mayresume replay of the user inputs until the timestamp of the next userinput in the set is after the timestamp of the next screen capture imagein the set of images (2240). Then the system may repeat determiningwhether to abort the replay, whether the state has been achieved, orwhether to continue replaying the user actions. Replaying the user inputactions saves the user time as the replay may occur more quickly thanthe user actually performing the actions. Furthermore, replaying theuser input actions enables the user to switch mobile devices whilekeeping the same state, or to help another user achieve the same state,as will be described in more detail with regard to FIG. 23.

Process 2200 may be used to automatically repeat a task for the user.For example, the system may provide an interface that enables the userto choose a previous screen and indicate the user wishes to repeat theaction that led to the screen. In such an implementation, the system mayfind the set of user input actions, as described above. The system mayreplay the user input actions as above, except that for text inputactions, the system may not replay the actions but may obtain or waitfor input from the user. For example, the system may search the userinput actions in the set and determine the user input actions thatinclude a text input. The system may prompt the user for new text inputto replace the text input identified in the user input actions. Thesystem may use the new text input when replaying the user input actions.Such implementations allow the user, for example, to make a restaurantreservation by selecting the image from a previous reservation andproviding the date and time of the new reservation (e.g., via theinterface). In addition or alternatively, the user interface may enablethe user to indicate a recurring action, such as reserving a table for 8pm every Tuesday for some specified time frame. The system may thencalculate the date rather than asking the user for the date. In suchimplementations, process 2200 can shorten the number of key-presses andactions needed by the user to repeat an action.

In some implementations, the mobile device may provide the user inputactions and the set of screen capture images to a server. The server mayuse the user input actions and set of screen capture images as input toa machine learning algorithm, for example as training data. The machinelearning algorithm may be configured to predict future actions based onpast actions, and could be used to determine action events, as discussedabove. The user input actions and screen capture images may be treatedin one or more ways before it is stored or used at the server, so thatpersonally identifiable information is removed. For example, the datamay be treated so that no personally identifiable information can bedetermined for the user, or a user's geographic location may begeneralized where location information is obtained (such as to a city,ZIP code, or state level). In some implementations, the server mayperiodically provide the mobile device with coefficients and the mobiledevice may use the coefficients to execute an algorithm to predictlikelihood of user action so that the mobile device can make aprediction without communicating with the server for each prediction.The mobile device may periodically update the server with historicaldata, which the server may use to calculate updated coefficients. Theserver may provide the updated coefficients to the mobile device. Insome implementations, the user device may operate its own machinelearning algorithm to determine prediction coefficients, obviating theneed for communication with any other computer.

Sharing Screen Content in a Mobile Environment

Some implementations may provide a user with the capability of sharingcurrent screen content or previous screen content with others. Thesystem may enable the user to choose the areas of the screen to share.Because sharing works across all mobile applications running on themobile device, sharing a picture works the same as sharing a newsarticle, making the user experience more fluid and consistent. The usermay also choose to share a previously viewed screen. For example, theuser may be chatting with another person and desire to share apreviously viewed article. The user may search for the article, forexample using a search application for the index of previously capturedscreens, and select a screen showing the article from a search result.The user may then share the selected screen with the other person.Sharing may occur directly from mobile device to mobile device or via aserver. In a server implementation, sharing the screen may includecopying the screen from the sender's data to the recipient's data storeof shared screens and sending a notification that the screen is ready tobe viewed.

The recipient of a shared screen may view the shared screen as apicture. In some implementations, if the recipient's mobile device isalso running the screen capture application, the recipient's system maycapture the picture, index it, generate annotation data, etc. In someimplementations, receiving a shared screen may trigger an automatedresponse. One automated response may be to perform recognition on theimage and find a web page or other URL (e.g., document available via theInternet) that matches the recognized content. If a matching URL isfound, the system may open this URL in a browser application for therecipient or in a corresponding mobile application. For example, if theshared image came from a news application, for example a StateBroadcasting Company (SBC) application, the recipient's mobile devicemay use the SBC application to open the URL. In some implementations,the application used to capture the shared screen may be sent with theshared image so the recipient mobile device knows which application touse to open the URL. If the recipient does not have the mobileapplication installed, the browser application may be used, or therecipient's mobile device may ask if the recipient wants to install theapplication.

In another automated response, a user may send a shared screen and userinput actions (e.g., taps, swipes, text input) to the second device.This may allow the recipient device to automatically take the recipientdevice to a state represented by the shared image, as described abovewith regard to FIG. 22. Sharing a set of screens and a set of user inputactions may allow a user to switch mobile devices while keeping a state,or may allow a recipient to achieve the state of the sender. Of course,the system may only share user input actions when authorized by thesender. In some implementations, when a user sends multiple screenshots,e.g., a range of screen shots captured in a certain timeframe, thesystem may stitch the screens together so that the recipient receives alarger image that can be scrolled, rather than individual screens. Insome implementations the screen sharing mode may be automatic when theuser is in a particular application (e.g., the camera or photoapplication). In some implementations, the device may share the screeneach time a photo is taken. Automatic sharing may allow the user to postphotos automatically to a second device operated by a user or by afriend or family of the user.

FIG. 23 illustrates a flow diagram of an example process for sharing animage of screen content displayed on a mobile device, in accordance withdisclosed implementations. Process 2300 may be performed by a mobilecontent context system, such as system 100 of FIG. 1 or system 300 ofFIG. 3. Process 2300 may enable a user of a mobile device to share onescreen or a series of previously captured screen images with arecipient. The recipient may be another mobile device for the same useror may be a mobile device for a different user.

Process 2300 may begin when the system receives an instruction to sharean image of a screen captured from a display of a mobile device (2305).The instruction may be in the form of a gesture or an option in thenotification bar. The image may be an image of a screen currently beingdisplayed on the mobile device or may be an image from a previouslycaptured screen. For example, the image may be an image that is part ofa search result. In some implementations, the image may be a series ofimages taken over a period of time. In such implementations, the systemmay stitch together the series of images into a single image that isscrollable prior to sending the image. The system may determine whetherthe sender wants to edit the image to be shared prior to sending theimage (2310). In other words, the system may provide the sender with anopportunity to indicate portions of the image to send or portions of theimage not to send, e.g., portions to redact. If the sender wants to editthe image (2310, Yes) the system may provide the sender an interfacewhere the sender can select a portion of the image to share or canselect a portion of the image to redact (2315). In this manner thesystem enables the sender to redact or obscure information on the screencapture image prior to sharing the image or to share a limited portionof the image. Obscuring the image may include changing the image data inthe redacted portion. For example the system may change the image datato all high values or all low values. In some implementations, the imagemay be transmitted without the redacted portions. In someimplementations, the system may prompt the user to redact a portion. Forexample, if the user previously redacted a phone number before sharing ascreen, and the system identifies the same phone number in the image,the system may suggest that the user redact the phone number again.

The system may transmit the image and associated metadata to a specifiedrecipient mobile device (2320). The specified recipient mobile devicemay be a second mobile device operated by the sender, or can be a mobiledevice associated with another user. The metadata may include anapplication used to generate the screen image, a timestamp for theimage, etc. In some implementations, the metadata may also include userinput data associated with the image to be shared. For example, thesystem may provide the opportunity for the sender to indicate whether toshare information that enables the recipient mobile device toautomatically enter the state represented by the shared image. When theuser indicates state information may be shared, the system may providethe set of user input data that occurred between a timestamp associatedwith a reference screen and the timestamp associated with the sharedimage, as discussed above. The metadata may also include any previouslycaptured screen images with a timestamp between the timestamp for thereference image and the shared image. The image and associated metadatamay be shared directly from the sending mobile device to the recipientmobile device, e.g., using a cellular network or wireless network, etc.,or may be accomplished via a server. When the system uses a server as anintermediary, sending the image and associated metadata may includecopying the image and associated metadata from a user account for thesender to a user account for the recipient and sending a notification tothe recipient mobile device. In some implementations, sending the imageand associated metadata may include providing access to image on theserver to the recipient based on permissions established by the user.

At the recipient mobile device, the system may receive the image and theassociated metadata from the sender (2322). The system may determinewhether to perform an automated response in response to receiving theimage (2325). For example, if the recipient mobile device does not havethe screen capture mobile application installed or if the recipient hasdisabled automated responses on the mobile device, the system may notperform an automated response (2325, No) and the recipient mobile devicemay display the image as a picture or a mark-up document, such as anHTML document (2330). If the system displays the image as a mark-updocument, the system may annotate the mark-up document so that variousportions of the document are actionable For example, at a server thesystem may annotate the image with a generic profile and construct anHTML document from the image, making the entities actionable usingconventional mark-up techniques. Thus, the recipient may receive themark-up document rather than an image. If the recipient is running thescreen capture mobile application, the recipient mobile device maygenerate annotation data for the shared image and display the annotationdata for the image. Of course, the annotation data generated for therecipient may differ from any annotation data generated for the sameimage at the sending mobile device as the user context and preferencesdiffer. Process 2300 then ends, having successfully shared the screen.

If the received image does trigger an automated response (2325, Yes),the system may determine whether the metadata associated with thereceived image includes user input data (2335). If it does not (2335,No), the system may perform recognition on the received image, aspreviously described (2245). Recognized items in the received image maybe text characters or numbers, landmarks, logos, etc. identified usingvarious recognition techniques, including character recognition, imagerecognition, logo recognition, etc. The system may use the recognizeditems to find a source document for the received image usingconventional techniques. Such techniques are described in InternationalPatent Publication No WO 2012/075315 entitled “Identifying MatchingCanonical Documents in Response to a Visual Query,” the disclosure ofwhich is incorporated herein its entirety. The source document may berepresented by a URL. The recipient's mobile device may then navigate tothe URL in a browser application, or in other words open a window in thebrowser application with the URL. In some implementations, therecipient's mobile device may navigate to the URL using the mobileapplication identified in the metadata associated with the receivedimage. For example, if the sender was viewing a news article in an SBCmobile application, the recipient's system may use the SBC mobileapplication to open the article. If the recipient's mobile device doesnot have the corresponding mobile application installed, the system mayask the recipient to install the application or may use a browserapplication to view the URL. Of course, if a source document cannot belocated the recipient's mobile device may display the received image asdiscussed above with regard to step 2330.

When the metadata does include user inputs (2335, Yes), the system mayuse the user input data to replay the sender's actions to take therecipient's mobile device to a state represented by the shared image. Inother words, the recipient's mobile device may perform process 2000 ofFIG. 20 starting at step 2025, as the set of user input actions and theset of images are provided with the shared image to the recipient.Process 2300 then ends. Of course, the recipient's mobile device maycapture the displayed screen and index the screen as described above.Process 2300 may enable the user of two mobile devices may transfer thestate of one mobile device to the second mobile device, so that the usercan switch mobile devices without having to re-create the state, savingtime and input actions.

FIG. 25 shows an example of a generic computer device 2500, which may beoperated as system 100, and/or client 170 of FIG. 1, which may be usedwith the techniques described here. Computing device 2500 is intended torepresent various example forms of computing devices, such as laptops,desktops, workstations, personal digital assistants, cellulartelephones, smartphones, tablets, servers, and other computing devices,including wearable devices. The components shown here, their connectionsand relationships, and their functions, are meant to be examples only,and are not meant to limit implementations of the inventions describedand/or claimed in this document.

Computing device 2500 includes a processor 2502, memory 2504, a storagedevice 2506, and expansion ports 2510 connected via an interface 2508.In some implementations, computing device 2500 may include transceiver2546, communication interface 2544, and a GPS (Global PositioningSystem) receiver module 2548, among other components, connected viainterface 2508. Device 2500 may communicate wirelessly throughcommunication interface 2544, which may include digital signalprocessing circuitry where necessary. Each of the components 2502, 2504,2506, 2508, 2510, 2540, 2544, 2546, and 2548 may be mounted on a commonmotherboard or in other manners as appropriate.

The processor 2502 can process instructions for execution within thecomputing device 2500, including instructions stored in the memory 2504or on the storage device 2506 to display graphical information for a GUIon an external input/output device, such as display 2516. Display 2516may be a monitor or a flat touchscreen display. In some implementations,multiple processors and/or multiple buses may be used, as appropriate,along with multiple memories and types of memory. Also, multiplecomputing devices 2500 may be connected, with each device providingportions of the necessary operations (e.g., as a server bank, a group ofblade servers, or a multi-processor system).

The memory 2504 stores information within the computing device 2500. Inone implementation, the memory 2504 is a volatile memory unit or units.In another implementation, the memory 2504 is a non-volatile memory unitor units. The memory 2504 may also be another form of computer-readablemedium, such as a magnetic or optical disk. In some implementations, thememory 2504 may include expansion memory provided through an expansioninterface.

The storage device 2506 is capable of providing mass storage for thecomputing device 2500. In one implementation, the storage device 2506may be or include a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid-state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. A computer program product can be tangibly embodied insuch a computer-readable medium. The computer program product may alsoinclude instructions that, when executed, perform one or more methods,such as those described above. The computer- or machine-readable mediumis a storage device such as the memory 2504, the storage device 2506, ormemory on processor 2502.

The interface 2508 may be a high-speed controller that managesbandwidth-intensive operations for the computing device 2500 or a lowspeed controller that manages lower bandwidth-intensive operations, or acombination of such controllers. An external interface 2540 may beprovided so as to enable near area communication of device 2500 withother devices. In some implementations, controller 2508 may be coupledto storage device 2506 and expansion port 2514. The expansion port,which may include various communication ports (e.g., USB, Bluetooth,Ethernet, wireless Ethernet) may be coupled to one or more input/outputdevices, such as a keyboard, a pointing device, a scanner, or anetworking device such as a switch or router, e.g., through a networkadapter.

The computing device 2500 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 2530, or multiple times in a group of such servers. Itmay also be implemented as part of a rack server system. In addition, itmay be implemented in a computing device, such as a laptop computer2532, personal computer 2534, or tablet/smart phone 2536. An entiresystem may be made up of multiple computing devices 2500 communicatingwith each other. Other configurations are possible.

FIG. 26 shows an example of a generic computer device 2600, which may besystem 100 of FIG. 1, which may be used with the techniques describedhere. Computing device 2600 is intended to represent various exampleforms of large-scale data processing devices, such as servers, bladeservers, datacenters, mainframes, and other large-scale computingdevices. Computing device 2600 may be a distributed system havingmultiple processors, possibly including network attached storage nodes,that are interconnected by one or more communication networks. Thecomponents shown here, their connections and relationships, and theirfunctions, are meant to be examples only, and are not meant to limitimplementations of the inventions described and/or claimed in thisdocument.

Distributed computing system 2600 may include any number of computingdevices 2680. Computing devices 2680 may include a server or rackservers, mainframes, etc. communicating over a local or wide-areanetwork, dedicated optical links, modems, bridges, routers, switches,wired or wireless networks, etc.

In some implementations, each computing device may include multipleracks. For example, computing device 2680 a includes multiple racks 2658a-2658 n. Each rack may include one or more processors, such asprocessors 2652 a-2652 n and 2662 a-2662 n. The processors may includedata processors, network attached storage devices, and other computercontrolled devices. In some implementations, one processor may operateas a master processor and control the scheduling and data distributiontasks. Processors may be interconnected through one or more rackswitches 2658, and one or more racks may be connected through switch2678. Switch 2678 may handle communications between multiple connectedcomputing devices 2600.

Each rack may include memory, such as memory 2654 and memory 2664, andstorage, such as 2656 and 2666. Storage 2656 and 2666 may provide massstorage and may include volatile or non-volatile storage, such asnetwork-attached disks, floppy disks, hard disks, optical disks, tapes,flash memory or other similar solid state memory devices, or an array ofdevices, including devices in a storage area network or otherconfigurations. Storage 2656 or 2666 may be shared between multipleprocessors, multiple racks, or multiple computing devices and mayinclude a computer-readable medium storing instructions executable byone or more of the processors. Memory 2654 and 2664 may include, e.g.,volatile memory unit or units, a non-volatile memory unit or units,and/or other forms of computer-readable media, such as a magnetic oroptical disks, flash memory, cache, Random Access Memory (RAM), ReadOnly Memory (ROM), and combinations thereof. Memory, such as memory 2654may also be shared between processors 2652 a-2652 n. Data structures,such as an index, may be stored, for example, across storage 2656 andmemory 2654. Computing device 2600 may include other components notshown, such as controllers, buses, input/output devices, communicationsmodules, etc.

An entire system, such as system 100, may be made up of multiplecomputing devices 2600 communicating with each other. For example,device 2680 a may communicate with devices 2680 b, 2680 c, and 2680 d,and these may collectively be known as system 100. As another example,system 100 of FIG. 1 may include one or more computing devices 2600.Some of the computing devices may be located geographically close toeach other, and others may be located geographically distant. The layoutof system 2600 is an example only and the system may take on otherlayouts or configurations.

According to certain aspects of the disclosure, a mobile device includesat least one processor and memory storing a graph-based data store ofentities connected by edges and recognized items identified byperforming recognition on each of a plurality of images of screenscaptured on the mobile device. The memory may also store instructionsthat, when executed by the at least one processor, cause the mobiledevice select a set of images from the plurality of images, the setrepresenting a chronological window of time, each image in the sethaving a timestamp within the chronological window, and to identifyentities from the graph-based data store in the recognized items ofimages in a first portion of the window using recognized items forimages in a remaining portion of the window as context to disambiguateambiguous entity references.

These and other aspects can include one or more of the followingfeatures. For example, the first portion may be a middle portion and theremaining portion includes a front portion of images having a timestampprior to timestamps for images in the middle portion and a back portionof images having a timestamp after the timestamps for the images in themiddle portion. As another example, the memory may further store, foreach of the plurality of images, a mobile application associated withthe image and selecting the set of images includes using the associatedmobile applications to determine the set. As another example, the memorymay further store, for each of the plurality of images, a mobileapplication associated with the image and selecting the set of imagescan include determining that a next chronological image is associatedwith a mobile application that differs from a mobile applicationassociated with a last image in the set and closing the set in responseto the determining. In some such implementations, the first portion maybe a last portion and the remaining portion may include images having atimestamp prior to timestamps for images in the last portion.

As another example, the memory may further store, for each of theplurality of images, a mobile application associated with the image andselecting the set of images can include determining that a nextchronological image is associated with a first mobile application thatdiffers from a seconding mobile application associated with a last imagein the set, determining that the first mobile application launched thesecond mobile application, and including the next chronological image inthe set. As another example, the instructions further includeinstructions that, when executed by the at least one processor, causethe mobile device to determine, from the entities in the first portion,at least one entity that is an outlier and to store, in a user profilein the memory, the entities identified in the first portion except forthe at least one entity. As another example, the instructions mayfurther include instructions that, when executed by the at least oneprocessor, cause the mobile device to calculate a rank for a firstentity of the entities identified in the first portion based on anamount of time the first entity appears in the images. In some suchimplementations, calculating the rank can include determining that alocation of the first entity remains constant across images anddowngrading the rank in response to the determining.

According to certain aspects of the disclosure, a computer systemincludes at least one processor and memory storing instructions that,when executed by the at least one processor, cause the system toreceive, from a mobile device, an image of a screen displayed on themobile device, to identify recognized items for the image by performingrecognition on the image, and to repeat the receiving and identifyingfor a plurality of images. The instructions may also includeinstructions that, when executed by the at least one processor, causethe system to select a set of images from the plurality of images, theset representing a chronological window of time, each image in the sethaving a timestamp within the chronological window and to identifyentities appearing in the recognized items for images in a first portionof the window using the recognized items for images in a remainingportion of the window as context to disambiguate ambiguous entityreferences.

These and other aspects can include one or more of the followingfeatures. For example, the memory may further store instructions that,when executed by the at least one processor, causes the system todetermine that a first candidate entity and a second candidate entityeach correspond to a same recognized item for a first image in the firstportion of the window, to determine that the first candidate entityshares a category with a mobile application associated with the firstimage, and to select the first candidate entity over the secondcandidate entity based on sharing the category. In some suchimplementations, the second candidate entity may have a higher priorprobability than the first candidate entity. As another example, thememory may further store instructions that, when executed by the atleast one processor, causes the system to determine that a nextchronological image is associated with a mobile application that differsfrom a mobile application associated with a last image in the set and toclose the set in response to the determining. As another example, thememory may further store instructions that, when executed by the atleast one processor, causes the system to determine that a nextchronological image is associated with a first mobile application thatdiffers from a seconding mobile application associated with a last imagein the set, to determine that the first mobile application launched thesecond mobile application, and to include the next chronological imagein the set.

As another example, the instructions may also include instructions that,when executed by the at least one processor, cause the system tocalculate a rank for a first entity of the entities identified in thefirst portion based on an amount of time the first entity appears in theimages and to store the first entity with the rank in the memory. Insome such implementations, calculating the rank can include determiningthat a location of the first entity remains constant across images anddowngrading the rank in response to the determining.

According to certain aspects of the disclosure, a method includesreceiving an image captured from a mobile device display for a mobileapplication, determining a window that includes a chronological set ofimages, the images each representing a respective screen captured from adisplay of a mobile device and having an associated timestamp, andidentifying entities appearing in images in a first portion of thewindow using text for images in a remaining portion of the window ascontext to disambiguate ambiguous entity references.

These and other aspects can include one or more of the followingfeatures. For example, the first portion may be a middle portion and theremaining portion can include a front portion of images having atimestamp prior to timestamps for images in the middle portion and aback portion of images having a timestamp after the timestamps for theimages in the middle portion. As another example, selecting the set ofimages can include determining that a next chronological image isassociated with a mobile application that differs from a mobileapplication associated with a last image in the set and closing the setin response to the determining. In some such implementations, the firstportion may be a last portion and the remaining portion may includeimages having a timestamp prior to timestamps for images in the lastportion. As another example, selecting the set of images can includedetermining that a next chronological image is associated with a firstmobile application that differs from a seconding mobile applicationassociated with a last image in the set, determining that the firstmobile application launched the second mobile application, and includingthe next chronological image in the set.

As another example, the method may also include providing the identifiedentities to the mobile device for customizing a mobile applicationand/or the method may include calculating a rank for a first entity ofthe entities identified in the first portion based on an amount of timethe first entity appears in the images. In some implementations,calculating the rank can include determining that a location of thefirst entity remains constant and downgrading the rank in response tothe determining. As another example, the mobile application may be afirst mobile application and the method further includes determining aquantity of mobile applications associated with images having a firstentity and boosting a rank of the first entity when the first entity isassociated with more than one mobile application. As another example,the window may represent a fixed time period or the window may representa quantity of images.

According to certain aspects of the disclosure, a method includescapturing a screen displayed on a mobile device as an image, sending theimage to a server, and determining when to repeat the capturing andsending based on user interaction with the screen. These and otheraspects can include one or more of the following features. For example,determining when to repeat the capturing and sending can includerepeating the capturing and sending at a first interval when the screendisplayed on the mobile device is static and repeating the capturing andsending at a second interval when the screen is not static. In additionor optionally, the second interval is no longer than one second. Asanother example, the method may also include receiving data from theserver generated from the image and using the data to personalize thescreen displayed to the user. As another example, the method may alsoinclude identifying entities from a data graph that appear in contentrepresented by the captured screen and storing, in a user profile, theentities identified.

Various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium”“computer-readable medium” refers to any non-transitory computer programproduct, apparatus and/or device (e.g., magnetic discs, optical disks,memory (including Read Access Memory), Programmable Logic Devices(PLDs)) used to provide machine instructions and/or data to aprogrammable processor.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (“LAN”), a wide area network (“WAN”), and theInternet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

A number of implementations have been described. Nevertheless, variousmodifications may be made without departing from the spirit and scope ofthe invention. In addition, the logic flows depicted in the figures donot require the particular order shown, or sequential order, to achievedesirable results. In addition, other steps may be provided, or stepsmay be eliminated, from the described flows, and other components may beadded to, or removed from, the described systems. Accordingly, otherimplementations are within the scope of the following claims.

What is claimed is:
 1. A mobile device comprising: at least oneprocessor; and memory storing: a data store of entities, recognizeditems identified by performing recognition on each of a plurality ofimages of screens captured on the mobile device, each recognized itembeing associated with an image of the plurality of images, andinstructions that, when executed by the at least one processor, causethe mobile device to: select a set of images from the plurality ofimages, the set representing a chronological window of time, each imagein the set having a timestamp within the chronological window, andidentify entities from the data store in the recognized items of imagesin a first portion of the window using recognized items for images in aremaining portion of the window as context to disambiguate ambiguousentity references.
 2. The mobile device of claim 1, wherein the firstportion is a middle portion and the remaining portion includes a frontportion of images having a timestamp prior to timestamps for images inthe middle portion and a back portion of images having a timestamp afterthe timestamps for the images in the middle portion.
 3. The mobiledevice of claim 1, wherein the memory further stores, for each of theplurality of images, a mobile application associated with the image andselecting the set of images includes: using the associated mobileapplications to determine the set.
 4. The mobile device of claim 1,wherein the memory further stores, for each of the plurality of images,a mobile application associated with the image and selecting the set ofimages includes: determining that a next chronological image isassociated with a mobile application that differs from a mobileapplication associated with a last image in the set; and closing the setin response to the determining.
 5. The mobile device of claim 4, whereinthe first portion is a last portion and the remaining portion includesimages having a timestamp prior to timestamps for images in the lastportion.
 6. The mobile device of claim 1, wherein the memory furtherstores, for each of the plurality of images, a mobile applicationassociated with the image and selecting the set of images includes:determining that a next chronological image is associated with a firstmobile application that differs from a second mobile applicationassociated with a last image in the set; determining that the firstmobile application launched the second mobile application; and includingthe next chronological image in the set.
 7. The mobile device of claim1, wherein the instructions further include instructions that, whenexecuted by the at least one processor, cause the mobile device to:determine, from the entities in the first portion, at least one entitythat is an outlier; and store, in a user profile in the memory, theentities identified in the first portion except for the at least oneentity.
 8. The mobile device of claim 1, wherein the instructionsfurther include instructions that, when executed by the at least oneprocessor, cause the mobile device to: calculate a rank for a firstentity of the entities identified in the first portion based on anamount of time the first entity appears in the images.
 9. The mobiledevice of claim 8, wherein calculating the rank includes: determiningthat a location of the first entity remains constant across images; anddowngrading the rank in response to the determining.
 10. A computersystem comprising: at least one processor; and memory storinginstructions that, when executed by the at least one processor, causethe system to: receive, from a mobile device, an image of a screendisplayed on the mobile device, identify recognized items for the imageby performing recognition on the image, repeat the receiving andidentifying for a plurality of images, select a set of images from theplurality of images, the set representing a chronological window oftime, each image in the set having a timestamp within the chronologicalwindow, and identify entities appearing in the recognized items forimages in a first portion of the window using the recognized items forimages in a remaining portion of the window as context to disambiguateambiguous entity references.
 11. The system of claim 10, wherein thememory further stores instructions that, when executed by the at leastone processor, causes the system to: determine that a first candidateentity and a second candidate entity each correspond to a samerecognized item for a first image in the first portion of the window;determine that the first candidate entity shares a category with amobile application associated with the first image; and select the firstcandidate entity over the second candidate entity based on sharing thecategory.
 12. The system of claim 11, wherein the second candidateentity has a higher prior probability than the first candidate entity.13. The system of claim 10, wherein the memory further storesinstructions that, when executed by the at least one processor, causesthe system to: determine that a next chronological image is associatedwith a mobile application that differs from a mobile applicationassociated with a last image in the set; and close the set in responseto the determining.
 14. The system of claim 10, wherein the memoryfurther stores instructions that, when executed by the at least oneprocessor, causes the system to: determine that a next chronologicalimage is associated with a first mobile application that differs from asecond mobile application associated with a last image in the set;determine that the first mobile application launched the second mobileapplication; and include the next chronological image in the set. 15.The system of claim 10, wherein the instructions further includeinstructions that, when executed by the at least one processor, causethe system to: calculate a rank for a first entity of the entitiesidentified in the first portion based on an amount of time the firstentity appears in the images; and store the first entity with the rankin the memory.
 16. The system of claim 15, wherein calculating the rankincludes: determining that a location of the first entity remainsconstant across images; and downgrading the rank in response to thedetermining.
 17. A method comprising: selecting a set of images from aplurality of images of mobile application user interfaces captured whilethe mobile application was active, the set representing a chronologicalwindow of time, each image in the set having a timestamp within thechronological window; identifying recognized items in the set of imagesby performing recognition on each of the images, each recognized itembeing associated with an image of the set of images; and identifyingentities from an entity data store in the recognized items of images ina first portion of the window using recognized items for images in aremaining portion of the window as context to disambiguate ambiguousentity references.
 18. The method of claim 17, further comprising:calculating a rank for a first entity of the entities identified in thefirst portion based on an amount of time the first entity appears in theimages.
 19. The method of claim 18, wherein calculating the rankincludes: determining that a location of the first entity remainsconstant across images; and downgrading the rank in response to thedetermining.
 20. The method of claim 17, wherein the images are capturedresponsive to a touch event and responsive to a transition from oneapplication to another.